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Flaw in Grandstream VoIP telephones permits stealthy eavesdropping

Technology

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February 19, 2026

Flaw in Grandstream VoIP telephones permits stealthy eavesdropping

A crucial vulnerability in Grandstream GXP1600 sequence VoIP telephones permits a distant, unauthenticated attacker to achieve root privileges and silently snoop on communications.

VoIP communication tools from Grandstream Networks is being utilized by small and medium companies. The maker’s GXP product line is a part of the corporate’s high-end providing for companies, faculties, motels, and Web Telephony Service Suppliers (ITSP) world wide.

The vulnerability is tracked as CVE-2026-2329 and obtained a crucial severity rating of 9.3. It impacts the next six fashions of the GXP1600 sequence of units that run firmware variations previous to 1.0.7.81:

GXP1610

GXP1615

GXP1620

GXP1625

GXP1628

GXP1630

Even when a weak gadget isn’t immediately reachable over the general public web, an attacker can pivot to it from one other host on the community. Exploitation is silent, and all the pieces works as anticipated.

In a technical report, Rapid7 researchers clarify that the issue is within the gadget’s web-based API service (/cgi-bin/api.values.get), which is accessible with out authentication within the default configuration.

The API accepts a ‘request’ parameter containing colon-delimited identifiers, which is parsed right into a 64-byte stack buffer with out performing a size verify when copying characters into the buffer.

Due to this, an attacker supplying overly lengthy enter may cause a stack overflow, overwriting adjoining reminiscence to achieve management over a number of CPU registers, such because the Program Counter.

Rapid7 researchers developed a working Metasploit module to exhibit unauthenticated distant code execution as root by exploiting CVE-2026-2329.

Exploitation permits arbitrary OS command execution, extracting saved credentials of native customers and SIP accounts, and reconfiguring the gadget to use a malicious SIP proxy that enables eavesdropping on calls.

**Stealing credentials**
*Supply: Rapid7*

Rapid7 researchers say that profitable exploitation requires writing a number of null bytes to assemble a return-oriented programming (ROP) chain. Nevertheless, CVE-2026-2329 permits writing of just one null terminator byte throughout the overflow.

To bypass the restriction, the researchers used a number of colon-separated identifiers to set off the overflow repeatedly and write null bytes a number of instances.

“Each time a colon is encountered, the overflow could be triggered a subsequent time by way of the following identifier,” clarify the researchers within the technical writeup.

“We are able to leverage this, and the power to jot down a single null byte because the final character within the present identifier being processed, to jot down a number of null bytes throughout exploitation.”

The researchers contacted Grandstream on January 6 and once more on January 20 after receiving no response.

Finally, Grandstream mounted the problem on February 3, with the discharge of firmware model 1.0.7.81.

Technical particulars and a module for the Metasploit penetration testing and exploitation framework. Customers of weak Grandstream merchandise are strongly suggested to use accessible safety updates as quickly as potential.

Fashionable IT infrastructure strikes quicker than handbook workflows can deal with.

On this new Tines information, find out how your group can scale back hidden handbook delays, enhance reliability by automated response, and construct and scale clever workflows on prime of instruments you already use.

Astonishing Spinosaur Unearthed in The Sahara Is In contrast to Any Seen Earlier than : ScienceAlert

Science

Dr. Mike

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February 19, 2026

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Astonishing Spinosaur Unearthed in The Sahara Is In contrast to Any Seen Earlier than : ScienceAlert

A brand new Spinosaurus species has been unearthed from the Saharan desert, and its cranium bears an impressive crest by no means seen earlier than on this type of dinosaur.

Paleontologists have named it Spinosaurus mirabilis, which means ‘fantastic backbone lizard’. We heartily agree.

Paleoartist rendering of Spinosaurus mirabilis consuming a coelacanth. (Dani Navarro)

The invention reveals extra than simply the dinosaur’s magnificence, nonetheless. Spinosaurus have largely been present in coastal deposits, whereas this new specimen hails from deep inland in Niger, a whole bunch of kilometers from any ocean.

Even the paleontology staff, led by Paul Sereno of the College of Chicago, was caught off guard.

“This discover was so sudden and wonderful, it was actually emotional for our staff,” Sereno says.

“I will ceaselessly cherish the second in camp once we crowded round a laptop computer to have a look at the brand new species for the primary time… One member of our staff generated 3D digital fashions of the bones we discovered to assemble the cranium – on solar energy in the midst of the Sahara. That is when the importance of the invention actually registered.”

With its spiky, interlocking tooth harking back to fashionable crocodiles, and its proximity to long-necked dinosaurs buried in close by river sediments, Sereno and staff suppose this Spinosaurus may need led a semi-aquatic way of life amidst a forested habitat.

“I envision this dinosaur as a type of ‘hell heron’ that had no drawback wading on its sturdy legs into two meters of water however most likely spent most of its time stalking shallower traps for the numerous giant fish of the day,” Sereno says.

Associated: Fossil Discovery Rewrites International Dinosaur Historical past

The scimitar-shaped crest positive is good-looking, however precisely what goal it served stays a thriller. The staff suspect it was as soon as sheathed in keratin – maybe brightly coloured, like a toucan’s invoice – to create a type of visible show.

The analysis was printed in Science.

Consideration, Human Verification and Congestion, or Some Issues From Too A lot Higher Work

Econometrics

Dr. Mike

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February 19, 2026

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Consideration, Human Verification and Congestion, or Some Issues From Too A lot Higher Work

That is a part of my ongoing Claude Code sequence, that are substack posts discussing what I’m studying about Claude Code because it pertains to quantitative social scientists whose work lives inside folders and directories on their native machines. My declare continues to be that in the meanwhile, there’s a surplus of writings about Claude Code by engineers for engineers, and a paucity of writings about Claude Code by social scientists for social scientists. So I’m simply documenting what I’m noticing, typically doing video walkthroughs, typically writing essays, and this one is extra essays about coping with the necessity to discover verification programs now that productiveness is legitimately enhanced with Claude Code. All Claude Code posts stay free once they come out, not like different posts are randomly paywalled. Every little thing goes behind a paywall after a couple of days, although. For those who discover this sequence invaluable, I encourage you to help it at $5/month or $50/12 months!

As with so lots of Claude Code posts, they’re pretty stream of consciousness. And this one isn’t any completely different. The fabric on this substack is kind of an concept I’m understanding which is printed on this deck which is that even with sustaining the identical quantity of human time on analysis, I believe there are such a lot of new issues with utilizing Claude Code for analysis that we might very nicely be in a really laborious place the place we have now to spend way more time on non-research associated actions making an attempt to resolve these new issues that we aren’t used to encountering.

On this deck, I’ve been understanding the issues I’m creating for myself with a lot new, larger high quality analysis output, the place I’m inadvertently creating too many actions. And within the technique of seeing productiveness features, however all the time with diminishing marginal returns, these new prices are exploding round me in methods I’m not anticipating. I name them all through “inventory pollution”, virtually like litter, and I’m making an attempt to determine which ones are simply tolerable, and which of them are completely not tolerable.

Hyper Systematic Group Interacting with Unusual Consideration

So again to the issue. Over the previous few weeks, Claude Code had helped me generate an enormous quantity of fabric for this undertaking that I had revived after months of sitting on it and procrastinating on the revision, making an attempt to faux I didn’t keep in mind the deadline was approaching. So I had used Claude Code to interrupt down particularly what the referees and editor wished achieved. The revised analyses, the robustness checks, new specs, figures, and so on. I used Claude Code to interrupt down exactly what they had been asking me to do, set up these into aichecklist, like a map of duties, in order that I’d not inadvertently skip something. After which we had one after the other began doing them.

And I might really feel my productiveness exploding as a result of it was — there have been some issues, many issues, that I used to be finishing immediately, and there have been issues that I additionally felt like I used to be getting achieved that was more durable to clarify. However it needed to do with how tousled I get due to my ADHD “primarily inattentive” stuff. How I can’t fairly keep in mind the place I’m in a course of, or how I get fascinated with essentially the most trivial particulars, going deeper and deeper, till I mainly nowhere close to the place I began. And to me this isn’t simply how I’m, but when I’m sincere, it’s how I wish to be too. I really like that hyper fixation, that stream state, when it occurs. I’m profoundly curious, to a fault, and in ways in which I believe annoy coauthors, however so usually they lead me to the enhancements. It’s simply that in addition they spew off lots of air pollution too, and so in my analysis initiatives, my coauthors usually must tolerate lots of it within the hopes that on common we’re getting someplace that can enhance issues. And my finest coauthor experiences don’t thoughts it, see the purpose of it, and are prepared to trip it with me.

So then in that sense the analysis with Claude Code has that very same function. It’s simply that my productiveness is ramped up by 5x, and since these issues are nonetheless there, it implies that the externalities are additionally generated at 5x. And I’m not likely positive that they’re the truth is linear within the work. I believe typically that the externalities could even nonlinear within the productiveness, and since my velocity of labor is now quicker, and in a brand new setting with out the guardrails I had spent years perfecting after graduate college to maintain me targeted and on observe with minimal errors and maximized output, the prices related to the progress would possibly very nicely be convex, rising quicker than the features.

So let me then share a bit of of what I’m pondering. I believe that my fashion of interacting with the analysis by way of my “rhetoric of decks” philosophy, the place I hold fixed notes in a journal of an evolving scroll of “lovely decks”, largely including to them, may very well be creating some challenges. I can’t fairly put my finger on it, and haven’t but, however I believe the decks are obligatory for me, and but they’re additionally the supply of inventory pollution rising quick within the analysis course of, making discovering what I would like like discovering a needle in a haystack as I can’t appear to on the finish, when it’s time to complete this up, keep in mind the place issues are.

A few of it’s because as organized as Claude Code is, each single concept I give, he generates the code and shops it, however I’ve observed he could not all the time put it within the place I would like it. And never solely that, he’ll usually generate new code, quite than add to the present file I would like, which I believe could create these small random perturbations within the pipeline the place issues are branching off. This occurs most of all in outdated initiatives being revived I’ve observed because the outdated initiatives have legacy kinds of group that aren’t essentially what I’m doing now once I begin. As a result of once I begin now, I are inclined to have a a lot less complicated place to begin that appears like this.

That’s generated above utilizing my /newproject talent. It generates that listing construction for all new initiatives. However for outdated initiatives, like I stated, I can’t and don’t try this out of concern that I’ll overwrite issues, which is an actual fear I’ve with Claude Code — the inadvertent deleting of knowledge is one thing I explicitly inform Claude Code to not do in my static Claude.md markdown.

Which suggests although that my present R&Rs the place I’m brining Claude Code for AI help are messier than meant. Once I revive outdated initiatives, and attempt to carry it into the self-discipline of Claude Code, it seems a lot crazier as a result of it has this Frankenstein fashion hodge podge of the outdated and the brand new, and I discover I’m not prepared to only flip to the brand new fashion and as a substitute are inclined to grandfather within the outdated, which this undertaking was because it was an R&R, and which subsequently could or could not have contributed to the difficult-to-put-my-finger-on battle I used to be having preserving observe of simply what was occurring

Isoquants, Consideration, Misplaced Consideration

Recall that I gave this discuss to the Boston Fed again in mid-December, which now looks like I used to be making an attempt to carry lately found fireplace to them in mild of the fast explosion in Claude Code consciousness via the social sciences, however which on the time I used to be sort of apprehensive I used to be going to sound like a manic and considerably over-reacting seminar speaker stuffed with prophetic hopes and doomsday predictions. Nicely, each might be true. Anyway, right here was my primary framework when you didn’t learn earlier posts about this (which frankly are posts I used to write down way back to 2023).

Recall that my core conviction is that the isoquants from manufacturing capabilities for doing “inventive cognitive work” have flatted from being quasi-concave pre-AI — whereby it was unattainable to do any inventive cognitive work with out utilizing nontrivial quantities of human time — to having flattened, and for a lot of duties, truly linear which as economists know implies that if I’m proper, then machine time and human time are good substitutes, even for cognitively inventive duties.

Nicely if they’re the truth is good substitutes, then rational actors will use the cheaper of the 2 on the margin. We pay month-to-month costs for Claude Code at anyplace between $20 to $200. We don’t pay for tokens on a case foundation, however we do incur alternative price of human time on a case foundation (proxied by the worth we place on our subsequent finest various). And in order such there’s temptation when utilizing AI for analysis, virtually like we’re sporting heavy weights round our legs, for AI to tug us in direction of utilizing much less time on analysis. I don’t imply, although, doing much less analysis notice. I imply much less time. Much less human time on analysis, and if human time is a direct enter in consideration, as you can not take note of issues that you’re not actually focusing your time on, we are able to find yourself studying much less and doing extra on the identical time.

That is actually on the core of lots of the issues, other than ethics (although this certainly will get into ethics too as to what diploma are you the professional on issues you’re driving?), of utilizing AI for social scientific analysis. Lowered time resulting in lowered consideration, resulting in much less human capital, regardless of the completion of precise cognitively inventive duties is the place human researchers turn into roughly elective within the technique of doing analysis.

So I define three prospects, solely two of that are good for human researchers if our aim is to keep up a reference to the data we’re answerable for creating. And one among them — the primary one — is the one I personally maintain to which is that I preserve my time use dedicated to the analysis in order that I preserve my curiosity and studying, as a result of my curiosity is my energy, and if I stay a life the place I drift away from my love of studying, discovery and engagement with my curiosity, I would as nicely go discover a job some other place. I merely refuse to stay an inferior life the place I’m not engaged within the actions I really like, which is a connection to studying in all of the ways in which makes my coronary heart sing. That is partly what differentiates me from being purely somebody who cares about coverage for its personal sake — I’m a hedonist. I care about my passions and curiosity for its personal sake, and every part else will get swept together with it. I simply attempt to intention that short-term wants with long-term targets in order that I’m helped in ways in which contact on different values I’ve, like serving to folks.

And so this image is kind of what I see as my very own aspirational aim. Keep time use on analysis subjects utilizing AI in order that the productiveness features occur. That is represented to me as the perfect final result as a result of the output features are the most important on a per unit foundation. It’s the identical time, H*, it simply will inevitably be completely different time.

However the reality of the matter is that there’s a pull, like a gravity power, that pulls the researcher down and away from H*. And one among them is arguably welfare enhancing from the angle of elevated data for oneself, and the opposite shouldn’t be. The one on the left represents gained data with lowered time use, and the on the correct represents excessive automation the place time use fell an excessive amount of such that the human grew to become actually nothing greater than what I simply typically name the “button pusher”, the place analysis turns into manufacturing unit work.

And so what I used to be experiencing within the R&R was that particular manifestation of how during which utilizing Claude Code to help me within the analysis course of was mixing me concurrently amongst all three of those states. It was creating some sort of inner coordination drawback that I couldn’t fairly put my finger on, however I wished to now simply describe what I believe is going on.

The Drawback of Too A lot Higher Work

So a part of the issues I believe I’m having is that as I’m going so quick, growing my work by 5-10x, and utilizing “lovely decks” to keep up my connection to the progress, like a operating diary, I’m someway creating too many decks, with out of order progress. This occurs particularly for the really advanced initiatives, too. The place there could also be 5 methods of doing one thing and the place ex ante there isn’t a clear cause to favor one over the opposite, and so I do all 5 after which must determine how they are often reconciled, if they need to be reconciled, and how you can go about positioning these reveals. Do they go within the manuscript? In that case the place? In that case how will they be displayed? 5 tables? 5 figures? 5 panels? One panel? So I could attempt all choices for aesthetic functions, however I could too iterate sequentially as I do it, realizing that the correct strategy to do it’s to do XYZ, not realizing that that perception got here to me after some earlier step of ABC.

The issue in utilizing decks this strategy to preserve my reference to the work is small, delicate particulars. For one, Claude Code could virtually randomly laborious code the code output into the decks except I say in any other case. And if I’m not utilizing /talent instructions for repeated work, and if these /talent instructions haven’t been completely perfected to keep away from laborious coding into decks — one thing so particular it might be missed — chances are you’ll not notice that randomly all through the deck are non-replicable work.

See, if the work is tough coded into the deck, regardless that the output ./tex exists, then chances are you’ll very nicely have TWO copies of the identical factor — you could have the outdated copy that’s utilizing at-that-time output, and you could have a brand new copy in .tex generated from estout or outreg2.

So this has been a problem for me to resolve. How do I preserve a brand new diary of progress, sustaining my consideration, however now coping with the inventory pollution, let’s name it, of stuff surrounding me? If the truth is the manufacturing of extra waste is convex in time use, then maybe I’ve two issues taking place without delay — I’ve elevated productiveness, however diminishing marginal returns because the one legislation of economics, even moreso than demand sloping downward itself (however which is the truth is answerable for demand sloping downward when it does), is the legislation of diminishing marginal returns to human time. And I’ve convex price capabilities such that every further use of time will increase at an exponential price rising marginal prices alongside some dimension that I could circuitously perceive, however which via repeated interactions on this new setting I completely hold encountering.

Sustaining Consideration, Lowered Congestion and Human Verification Is The brand new Ability

I noticed Andrew Karpathy say lately, I’ll must dig up the quote, that the brand new talent is in human verification. It’s not in ‘vibe coding’ on this age of Claude Code as there’s mainly no obstacles to entry to telling Claude Code “do that and that”. There isn’t any talent in any respect in dictating “do that difficult partial identification factor I’ve all the time wished achieved”. That takes no talent, and since Claude Code is mainly a genius, compliant, and cussed like an obedient canine to do something and every part you ask of it, it’s going to do it.

The actual talent going ahead shouldn’t be subsequently within the doing. We’ll all be sitting with jet packs on our again, and that after we determine to elevate off, we’ll elevate off — simply not slowly. If we aren’t cautious, we’ll rip via our environment at mild velocity, and whereas it’s true we’ll get someplace quicker, we’ll shatter home windows and homes in our means too.

I’m targeted now on simply the extreme litter I’m creating in my decks with my new workflow that I can’t fairly get a bead on. And I’ve latched onto my “rhetoric of decks” as a result of I’m utilizing it lots to assist me hold observe of labor over time. However I’m subsequently coping with idiosyncratic issues too from that being an imperfect answer.

So to Kaparthy’s level, the brand new talent shouldn’t be within the doing. Quite it’s in a single space he identifies, and two extra that I’m targeted on.

Human verification. We’re answerable for every part. We should subsequently discover a strategy to insert 100% correct verification programs into the analysis course of. There might be no errors. And admittedly, given the issue of figuring out errors, I believe in an virtually Beckerian like means, the stigma and punishments aimed toward even the smallest AI-related errors going ahead are in all probability going to be draconian. Similar to in a footnote in “Crime and Punishment”, Becker’s basic 1968 JPE on the economics of crime, the place he notes that Vietnamese rice speculators had their arms minimize off for his or her crimes as a result of low possibilities of detection, I believe we’ll see much more of that going ahead. Science is many issues, however scientific communities have a tendency to manage their very own via sanctions and rewards.
Excessive Stage of Consideration. So we should be vigilant and even obsessive about zero error philosophies now greater than ever. And it’s actually unclear on these time use curves I drew simply the place that’s, and the place we have to be concerned and the place we don’t have to be concerned, and the way we are able to automate even the verification, and which elements can’t be automated in any respect. All I do know is that the ultimate product should be one thing all of us perceive simply as a lot as we ever did, which finest I can inform requires a excessive degree of consideration. I believe this positively means for many of us preserving human time use on the analysis undertaking as excessive as humanly potential and resist, and even refuse, automation of analysis. Not a lot as a result of we’re in precept dedicated to human work, however as a result of I don’t assume we’re even near a world the place robots have the comparative benefit in automating scientific discoveries. I doubt the isoquants are straight strains — but.
Congestion. However sustaining the identical degree of time use with out addressing these convex prices coming from the inventory pollution related to the identical sort of time use is I believe going to be its personal drawback to be solved. It’s associated, clearly, to the opposite two, however I believe it’s nonetheless useful to separate it out.

Which brings me again to all my “lovely decks”. I’m not saying that the fault is in my deck philosophy — of utilizing decks to maintain me connected. A few of what I outlined, in spite of everything, is completely fixable via new workflows the place I all the time use exported .tex recordsdata it doesn’t matter what.

However I nonetheless assume I see the issue a bit extra clearly from these overflowing decks as a result of sooner or later, for any typical analysis undertaking, I will find yourself with too many slides, and regardless of how “lovely” these slides are, I’ll find yourself with congestion, and I’ll have a tough time pin pointing precisely the place that congestion is happening.

So I’ll finish there. Among the ongoing video stroll throughs I believe might be much less about me doing as it’s about me coping with the issues of my doing. I’ll clearly be doing issues. I’ve a cool new video sequence that I wish to announce however am ready a bit longer to take action. However I believe what you will note is me stumbling round, in actual time, making an attempt to doc the character of those rising marginal prices, after which making stabs at making an attempt to shift them down.

However that’s it for at the moment. Have an excellent day! Let’s hope we are able to hold going with none accidents!

DBMS Information Fashions Defined: Varieties and SQL Examples

Machine Learning

Dr. Mike

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February 19, 2026

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DBMS Information Fashions Defined: Varieties and SQL Examples

Fashionable purposes depend on structured storage programs that may scale, keep dependable, and maintain information constant. On the coronary heart of all of it sits the info mannequin. It defines how info is organized, saved, and retrieved. Get the mannequin improper and efficiency suffers, integrity breaks down, and future adjustments turn out to be painful. Get it proper and every little thing else turns into simpler to handle.

Right here, we’ll take a sensible take a look at database information fashions, from sorts and abstraction ranges to normalization and design. We’ll stroll by how an ER diagram turns into actual tables, utilizing SQL and actual situations to floor the idea. On this article, we’ll bridge DBMS ideas with hands-on database design.

What Is a Information Mannequin in DBMS?

A knowledge mannequin defines the logical construction of a database. The system defines how information components throughout the database system will join with one another whereas sustaining particular constraints. For instance, an information mannequin demonstrates {that a} scholar entity incorporates attributes similar to StudentID and Title whereas exhibiting {that a} Course entity connects to Scholar by an enrollment relationship. The mannequin defines which information we maintain and the foundations that regulate its administration.

Information fashions allow groups to create information illustration plans by logical design as an alternative of beginning with SQL tables. The strategy decreases errors whereas enhancing communication and making subsequent modifications simpler.

Key roles of an information mannequin embrace:

Construction: The system wants to rearrange information into entities and fields which signify tables and columns in a coherent construction.
Relationships: The system reveals how information components join with one another by its means to precise that college students can enroll in a number of programs whereas programs can have a number of college students enrolled in them.
Constraints: The system establishes information validation requirements by main keys which guarantee distinctive information identification and international keys which keep referential information relationships.
Abstraction: The system offers customers with an information idea interface which permits them to entry information by ideas like “scholar” as an alternative of needing to know file storage or disk association.

Sorts of Information Fashions in DBMS

Various kinds of information fashions exist in DBMS. This displays the best way wherein information is saved in accordance with the character of the info. Every mannequin has its personal manner of representing information:

Information exists in a hierarchical construction which varieties a tree sample. Each report within the system requires one dad or mum connection aside from the foundation report whereas the report could have a number of little one connections. Hierarchical constructions describe each XML paperwork and organizational charts. The system performs quick one-to-many searches however struggles with a number of connections between two entities.

 
   John Carter

The community construction shops information as a graph which represents a community of interconnected data. The system helps a number of dad or mum and little one hyperlinks for every report which creates pure many-to-many relationships. The system allows customers to create connections between components however it requires customers to deal with advanced strategies for each querying and system repairs.

Nearly all of database administration programs use the relational mannequin as their main database construction. Databases keep information in tables that are structured as relations that comprise each rows and columns. International keys set up connections between tables. The database mannequin provides customers a number of versatile choices which allow them to create advanced SQL database queries.

SELECT e.EmployeeName, p.ProjectID, p.StartDate 
FROM Worker e 
JOIN Venture p ON e.EmployeeID = p.EmployeeID;

The thing-oriented mannequin combines database know-how with object-oriented programming. The system shops information as objects which comprise each state info and operational strategies. The thing mannequin allows purposes to make use of customary inheritance and encapsulation mechanisms which assist them handle complexity.

NoSQL and Different Fashions:

Organizations require NoSQL database programs as a result of their information necessities demand each intensive capability and versatile storage. The programs function with out strict schema constructions. Doc shops use digital paperwork which observe the JSON construction as the idea for his or her record-keeping system whereas key-value shops present primary search features. Column-family shops use large desk constructions whereas graph databases use node and edge fashions to signify their information.

{ 
   "EmployeeName": "John Carter", 
   "Initiatives": [ 
    { 
           "ProjectName": "AI Dashboard", 
           "DurationMonths": 6
    } 
   ]
}

Information Modeling Abstraction Ranges

Information modeling is commonly described in three abstraction layers (generally known as the three-schema structure):

The best stage of this technique offers full information protection with none technical features. The conceptual mannequin defines high-level entities and relationships in enterprise phrases.

The reason expands by the identification of particular tables which comprise explicit columns and their related information sorts whereas remaining unbiased from any explicit database administration system. The logical mannequin takes the conceptual entities and lists their attributes and keys. The system shows main keys along with international keys whereas it offers information kind specs that embrace integer and string sorts with out addressing bodily implementation particulars.

Essentially the most full stage of element connects to a selected database administration system. The execution defines desk construction by its implementation particulars which embrace specs for column sorts and indexes and storage engines and partitions and different components.

CREATE INDEX idx_order_customer ON Orders(CustomerID); 
SELECT indexname, indexdef 
FROM pg_indexes 
WHERE tablename="orders";

Key Parts of a DBMS Information Mannequin

The elemental components of information fashions function their important elements. The research of those elements offers design capabilities that may obtain excessive efficiency and exact outcomes.

Entities and Attributes: Entities signify real-world objects similar to college students or programs. Attributes describe entity properties like title, e mail, or course title. The attribute definitions present clear descriptions which assist to remove uncertainty and make information validation simpler.

Relationships and Cardinality: Relationships set up the connections that hyperlink completely different entities. Cardinality defines the variety of components that may exist inside a specific relationship.

The three essential relationship sorts include:

One-to-One relationships
One-to-Many relationships
Many-to-Many relationships

The system enforces constraints which safeguard information integrity by their established guidelines.

Main Key: The first key features as a singular identifier that distinguishes all data inside a desk. The system prevents duplicate entries whereas it offers quick entry by indexing.
International Key: The international key establishes a connection between two related tables. The system maintains referential integrity by blocking any makes an attempt to create invalid hyperlinks.
Distinctive and Test Constraints: Distinctive constraints stop duplicate values. Test constraints validate information ranges or codecs.

The Entity-Relationship (ER) Mannequin

The Entity-Relationship (ER) mannequin serves as a broadly used methodology for creating conceptual fashions. The mannequin allows the illustration of precise objects by entities which show their inside construction. An entity corresponds to an object or idea (e.g. Scholar or Course), every with attributes (like StudentID, Title, Age).

A number of entities join by a relationship (like Enrollment) which reveals their relationship by describing their mutual actions (as an example, “a scholar enrolls in programs”).

The ER mannequin captures the essence of the info with out committing to a desk structure. The connection between Scholar and Course reveals a many-to-many connection which we will signify by a diagram.

A relational system transforms entities into tables whereas attributes turn out to be columns, and international keys serve to ascertain relationships between entities.

Key Parts (Main/International Keys, Constraints)

A Main Secret’s a singular identifier for desk rows. For instance, StudentID uniquely identifies every scholar. A main key column can not comprise NULL and have to be distinctive. It ensures we will all the time inform data aside.

student_id INT PRIMARY KEY

A International Secret’s a column or set of columns that hyperlinks to the first key of one other desk. This creates a referential integrity rule: the DBMS is not going to enable an enrollment that factors to a non-existent scholar. In SQL, we’d write:

FOREIGN KEY (StudentID) REFERENCES Scholar(StudentID)

Different constraints like NOT NULL, UNIQUE, or CHECK can implement information guidelines (e.g., a grade column have to be between 0 and 100). These constraints maintain the info legitimate in accordance with the mannequin

ALTER TABLE Scholar 
ADD CONSTRAINT unique_name UNIQUE (student_name);

Pattern Scholar Administration Database (MySQL Instance)

So for demonstration let’s use a primary Scholar Administration System. The system consists of three entities that are Scholar and Course and Enrollment that serves because the hyperlink between college students and programs. We exhibit the MySQL relational schema setup by the next course of.

CREATE TABLE Scholar (
    StudentID INT AUTO_INCREMENT PRIMARY KEY,
    StudentName VARCHAR(100) NOT NULL,
    Main VARCHAR(50),
    Age INT
);

CREATE TABLE Course (
    CourseID INT AUTO_INCREMENT PRIMARY KEY,
    CourseName VARCHAR(100) NOT NULL,
    Division VARCHAR(50)
);

CREATE TABLE Enrollment (
    EnrollmentID INT AUTO_INCREMENT PRIMARY KEY,
    StudentID INT NOT NULL,
    CourseID INT NOT NULL,
    Grade CHAR(2),
    FOREIGN KEY (StudentID) REFERENCES Scholar(StudentID),
    FOREIGN KEY (CourseID) REFERENCES Course(CourseID)
);

On this schema:

The StudentID and CourseID function main keys for his or her respective tables which leads to each scholar and course receiving distinct identification numbers.
The Enrollment desk has two international keys (StudentID, CourseID) that reference the respective main keys. This enforces that each enrollment entry corresponds to a legitimate scholar and course.
The AUTO_INCREMENT attribute (MySQL-specific) mechanically generates distinctive IDs. The NOT NULL constraint ensures these ID fields will need to have values.
Different constraints like NOT NULL on names stop lacking information.

This design is helps in creating normalization, so scholar and course info isn’t duplicated in every enrollment row, decreasing redundancy

Inserting Pattern Information

INSERT INTO Scholar (StudentName, Main, Age) VALUES
    ('Alice', 'Biology', 20),
    ('Bob', 'Pc Science', 22);

INSERT INTO Course (CourseName, Division) VALUES
    ('Database Techniques', 'Pc Science'),
    ('Calculus I', 'Arithmetic');

INSERT INTO Enrollment (StudentID, CourseID, Grade) VALUES
    (1, 1, 'A'),
    (1, 2, 'B'),
    (2, 1, 'A');

These inserts add two college students and two programs. Then we add enrollments linking them: for instance, (1,1,’A’) means Alice (StudentID=1) takes Database Techniques (CourseID=1) and earned an A grade. MySQL enforces international key constraints which stop customers from including enrollments that comprise non-existent StudentID or CourseID values. Our pattern information exists in third Regular Type (3NF) as a result of each information aspect exists as a single storage merchandise.

Normalization in DBMS

Normalization organizes tables by its course of which eliminates duplicate information and prevents points throughout updates. The conventional varieties guidelines which we make the most of to implement our system embrace the next definitions:

1NF (First Regular Type): Every desk cell ought to maintain a single worth (no repeating teams).
2NF (Second Regular Type): In tables with composite keys, non-key columns should rely upon the entire key, not simply a part of it.
3NF (Third Regular Type): Non-key columns should rely solely on the first key, not on different non-key columns.

The method of normalization brings two advantages as a result of it decreases information duplication which results in storage financial savings and prevents information inconsistencies whereas making information upkeep simpler. The Scholar desk serves as the one supply for updating Alice’s main and age info. The method of information normalization creates advantages however its extremely standardized schemas require a number of JOIN to construct report information which causes delays in executing advanced queries.

Benefits and Disadvantages of Information Fashions

Benefits	Disadvantages
Guarantee correct and constant illustration of information	Preliminary design requires important time for advanced programs
Scale back information redundancy and keep away from duplication	Massive schemas turn out to be obscure
Main and international keys set up clear relationship definitions	Minor structural adjustments can impression the whole system
Enhance information integrity by constraints and guidelines	Requires experience in each area data and database programs
Make databases extra comprehensible for builders and analysts	Extremely dynamic programs could endure from over-engineered fashions
Assist ongoing upkeep and future enlargement

Conclusion

The muse of any reliable database system is determined by its information fashions which function basic elements. They help in creating databases which meet precise wants by their structured design and skill to deal with growing information volumes and obtain operational effectivity. Understanding conceptual and logical and bodily fashions allows you to handle system information conduct. Database upkeep turns into less complicated and question execution hurries up by correct implementation of modeling and normalization and indexing strategies. Information modeling requires funding of time as a result of it advantages each small purposes and enormous enterprise programs.

Incessantly Requested Questions

Q1. What’s the goal of an information mannequin in DBMS?

A. It defines how information is structured, associated, and constrained, serving as a blueprint for constructing dependable and environment friendly databases.

Q2. What’s the distinction between conceptual, logical, and bodily fashions?

A. Conceptual focuses on enterprise entities, logical defines tables and keys, and bodily specifies implementation particulars like information sorts and indexes.

Q3. Why is normalization essential in database design?

A. It reduces redundancy, prevents replace anomalies, and improves information integrity by organizing information into well-structured tables.

Whats up! I am Vipin, a passionate information science and machine studying fanatic with a powerful basis in information evaluation, machine studying algorithms, and programming. I’ve hands-on expertise in constructing fashions, managing messy information, and fixing real-world issues. My purpose is to use data-driven insights to create sensible options that drive outcomes. I am desperate to contribute my expertise in a collaborative atmosphere whereas persevering with to be taught and develop within the fields of Information Science, Machine Studying, and NLP.

Login to proceed studying and luxuriate in expert-curated content material.

WinterTC: Write as soon as, run wherever (for actual this time)

IT

Dr. Mike

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February 19, 2026

0

WinterTC: Write as soon as, run wherever (for actual this time)

The WinterCG group group was lately promoted to a technical committee, signaling a rising maturity for the usual that goals to solidify JavaScript runtimes. Now could be good time to meet up with this key function of recent JavaScript and the net growth panorama.

The WinterTC manifesto

To know what WinterTC is about, we will start with the committee’s personal manifesto:

The final word purpose of this committee is to advertise runtimes supporting a complete unified API floor that JavaScript builders can depend on, no matter whether or not their code can be utilized in browsers, servers, or edge runtimes.

What’s notable right here is that it was solely very lately that the JavaScript server-side wanted unification. For over a decade, this house was simply Node. These days, now we have a rising abundance of runtime choices for JavaScript and TypeScript; choices embody Node, Deno, Bun, Cloudflare Employees, serverless platforms like Vercel and Netlify, and cloud environments like AWS’s LLRT. Whereas this selection signifies a wholesome response to the calls for of recent internet growth, it additionally results in fragmentation. As builders, we might discover ourselves managing fixed psychological friction: pressured to fret in regards to the the place reasonably than the what.

Additionally see: The whole information to Node.js frameworks.

WinterTC proposes to easy out these onerous edges by making a baseline of assured API floor throughout all JavaScript runtimes. It’s a undertaking whose time has come.

Ecma TC55: The committee for interoperable internet runtimes

WinterTC isn’t only a hopeful suggestion; it’s an official customary that any runtime price its salt might want to fulfill. WinterTC (formally Ecma TC55) is a technical committee devoted to interoperable internet runtimes. It sits alongside TC39, the committee that standardizes JavaScript itself.

WinterTC is a form of peace treaty between the key gamers within the internet runtimes house—Cloudflare, Vercel, Deno, and the Node.js core workforce.

The primary perception of TC55, which underpins the options it seeks, is easy: The browser is the baseline.

As a substitute of inventing new server-side requirements, like a brand new strategy to deal with HTTP requests, WinterTC mandates that servers undertake browser requirements (an strategy that profitable APIs like fetch had already pushed into de facto requirements). It creates a form of common customary library for JavaScript that exists exterior the browser however gives the identical companies.

The convergence

To know what this new standardization means for builders, we will have a look at the code. For a very long time, server-side and client-side code relied on totally different dialects:

Browser: fetch for networking, EventTarget for occasions, and internet streams.
Node: http.request, EventEmitter, and Node streams.

The server has progressively absorbed the browser method, and is now standardized by WinterTC:

fetch: The common networking primitive is now customary on the again finish.
Request / Response: These customary HTTP objects (initially from the Service Employee API) now energy server frameworks.
World objects: TextEncoder, URL, Blob, and setTimeout work identically in every single place.

This convergence finally results in the conclusion of the “isomorphic JavaScript” promise. Isomorphic, which means the server and consumer mirror one another. Now you can write a validation operate utilizing customary URL and Blob APIs and run the very same file on the consumer (for UI suggestions) and the server (for onerous safety).

I believed isomorphic JavaScript was on the horizon when Node got here out, and I used to be not alone. Higher late than by no means.

The brand new server battlefields

When each runtime is trending towards supporting the identical APIs, how do they proceed to differentiate themselves? If code is absolutely moveable, the runtimes can now not compete on API availability (and even worse, on API lock-in). As a substitute, very similar to internet frameworks, they need to compete on the premise of developer expertise.

We’re seeing distinctive profiles emerge for every runtime:

Bun (tooling + velocity): Bun isn’t only a runtime; it’s an all-in-one bundler, check runner, and bundle supervisor. Its different promoting level is uncooked velocity.
Deno (safety + enterprise): Deno focuses on safety (with its opt-in permission system) and a “zero-config” developer expertise. It has discovered a robust area of interest powering the so-called Enterprise edge. It additionally has the Deno Contemporary framework.
Node (familiarity + stability): Node’s asset is its huge legacy ecosystem, reliability, and sheer familiarity. It’s catching up by adopting WinterTC requirements, however its major worth proposition is boring reliability—a function that holds appreciable weight within the growth world.

The cloud working system

WinterTC additionally has implications for the deployment panorama. Prior to now, you selected an working system; right this moment, you select a platform.

Platforms like Vercel and Netlify are progressively turning into a brand new OS layer. WinterTC acts because the POSIX for this rising cloud OS. Simply as POSIX allowed C code to run on Linux, macOS, and Unix, WinterTC permits JavaScript code to run on Vercel, Netlify, and Cloudflare with out a lot finagling.

Nevertheless, builders needs to be cautious of the brand new lock-in. Platforms can’t actually lock you in with the language anymore (WinterTC makes it simpler to swap deployment engines), however they will nonetheless lure you with knowledge. Companies like Vercel KV, Netlify Blobs, or Cloudflare D1 provide unimaginable comfort, however they’re proprietary. Your compute may be moveable, however your state shouldn’t be. Not that that is something new—databases, particularly managed ones, are inherently some extent of lock-in.

The poster baby: Hono

If you wish to see the standardized server in motion right this moment, look no additional than Hono. Hono is the Specific.js of the WinterTC world. It’s a light-weight internet framework that runs natively on Node, Deno, Bun, Cloudflare Employees, and Fastly, and even straight within the browser.

It’s necessary to notice that, whereas Hono is similar to Specific, it doesn’t use the acquainted Specific req and res objects. Specific objects are wrappers round Node-specific streams, IncomingMessage, and are mutable and intently tied to the Node runtime. Hono objects, in contrast, are the usual Fetch API Request and Response objects. They’re immutable and common. As a result of it’s constructed on these requirements, a Hono router seems to be acquainted to anybody who has used Specific, however it’s infinitely extra moveable:

import { Hono } from 'hono'
const app = new Hono()

app.get('/', (c) => {
  return c.textual content('Hiya InfoWorld!')
})

export default app

You possibly can deploy this code to a $5 DigitalOcean droplet operating Node, transfer it to a worldwide edge community on Cloudflare, and even run it inside a browser service employee to mock a again finish, all with out altering something.

The common adapter: Nitro

Whereas Hono represents the “pure” strategy (writing code that natively adheres to requirements), as builders, we frequently want extra energy and higher abstraction—issues like file-system routing, asset dealing with, and construct pipelines. That is the place Nitro is available in.

Nitro, which is a part of the UnJS ecosystem, is a form of common deployment adapter for server-side JavaScript. It’s the engine that powers frameworks like Nuxt and Analog, nevertheless it additionally works as a standalone server toolkit.

Nitro provides you a better order layer atop WinterTC. Nitro provides you further powers whereas smoothing out among the quirks that distinguish runtimes. For example, say you needed to make use of a particular Node utility, however you had been deploying to Cloudflare Employees. Nitro would routinely detect the goal surroundings and poly-fill the lacking options or swap them for platform-specific equivalents through the construct course of.

With Nitro, you may construct advanced, feature-rich purposes right this moment which might be prepared for the common, WinterTC pushed future.

Conclusion

By acknowledging the browser because the baseline, we’d lastly fulfill the promise of “write as soon as, run wherever.” We’ll lastly get our isomorphic JavaScript and drop the psychological overhead of context switching. There’ll all the time be a distinction between front-end and back-end builders, with the previous being concerned with view templates and reactive state and the latter touching the enterprise logic, file system, and datastores. However the actuality of the full-stack developer is turning into much less divisive on the language stage.

This motion is a part of an general maturation within the language, internet growth normally, and the server-side particularly. It feels just like the JavaScript server is lastly catching up with the browser.

Claude AI Utilized in Venezuela Raid: The Human Oversight Hole

Artificial Intelligence

Dr. Mike

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February 19, 2026

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Claude AI Utilized in Venezuela Raid: The Human Oversight Hole

Headlines

On February 13, the Wall Avenue Journal reported one thing that hadn’t been public earlier than: the Pentagon used Anthropic’s Claude AI through the January raid that captured Venezuelan Chief Nicolás Maduro.

It stated Claude’s deployment got here via Anthropic’s partnership with Palantir Applied sciences, whose platforms are extensively utilized by the Protection Division.

Reuters tried to independently confirm the report – they could not. Anthropic declined to touch upon particular operations. The Division of Protection declined to remark. Palantir stated nothing.

However the WSJ report revealed yet another element.

Someday after the January raid, an Anthropic worker reached out to somebody at Palantir and requested a direct query: how was Claude really utilized in that operation?

The corporate that constructed the mannequin and signed the $200 million contract needed to ask another person what their very own software program did throughout a navy assault on a capital metropolis.

This one element tells you the whole lot about the place we really are with AI governance. It additionally tells you why “human within the loop” stopped being a security assure someplace between the contract signing and Caracas.

How massive was the operation

Calling this a covert extraction misses what really occurred.

Delta Pressure raided a number of targets throughout Caracas. Greater than 150 plane have been concerned. Air protection methods have been suppressed earlier than the primary boots hit the bottom. Airstrikes hit navy targets and air defenses, and digital warfare belongings have been moved into the area, per Reuters.

Cuba later confirmed 32 of its troopers and intelligence personnel have been killed and declared two days of nationwide mourning. Venezuela’s authorities cited a demise toll of roughly 100.

Two sources informed Axios that Claude was used through the energetic operation itself, although Axios famous it couldn’t affirm the exact function Claude performed.

What Claude would possibly even have achieved

To grasp what might have been taking place, it is advisable to know one technical factor about how Claude works.

Anthropic’s API is stateless. Every name is unbiased i.e. you ship textual content in, you get textual content again, and that interplay is over. There is not any persistent reminiscence or Claude working repeatedly within the background.

It is much less like a mind and extra like an especially quick marketing consultant you may name each thirty seconds: you describe the scenario, they offer you their greatest evaluation, you hold up, you name once more with new info.

That is the API. However that claims nothing in regards to the methods Palantir constructed on prime of it.

You’ll be able to engineer an agent loop that feeds real-time intelligence into Claude repeatedly. You’ll be able to construct workflows the place Claude’s outputs set off the subsequent motion with minimal latency between advice and execution.

Testing These Eventualities Myself

To grasp what this really seems like in observe, I examined a few of these eventualities.

each 30 seconds. indefinitely.

The API is stateless. A classy navy system constructed on the API does not should be.

What that may seem like when deployed:

Intercepted communications in Spanish fed to Claude for fast translation and sample evaluation throughout a whole bunch of messages concurrently. Satellite tv for pc imagery processed to establish car actions, troop positions, or infrastructure adjustments with updates each jiffy as new photographs arrived.

Or real-time synthesis of intelligence from a number of sources – alerts intercepts, human intelligence studies, digital warfare knowledge – compressed into actionable briefings that might take analysts hours to supply manually.

skilled on eventualities. deployed in Caracas.

None of that requires Claude to “determine” something. It is all evaluation and synthesis.

However if you’re compressing a four-hour intelligence cycle into minutes, and that evaluation is feeding immediately into operational selections being made at that very same compressed timescale, the excellence between “evaluation” and “decision-making” begins to break down.

And since it is a labeled community, no one outdoors that system is aware of what was really constructed.

So when somebody says “Claude cannot run an autonomous operation” – they’re most likely proper in regards to the API stage. Whether or not they’re proper in regards to the deployment stage is a very totally different query. And one no one can at present reply.

Hole between autonomous and significant

Anthropic’s exhausting restrict is autonomous weapons – methods that determine to kill and not using a human signing off. That is an actual line.

However there’s an unlimited quantity of territory between “autonomous weapons” and “significant human oversight.” Take into consideration what it means in observe for a commander in an energetic operation. Claude is synthesizing intelligence throughout knowledge volumes no analyst might maintain of their head. It is compressing what was a four-hour briefing cycle into minutes.

this took 3 seconds.

It is surfacing patterns and suggestions quicker than any human staff might produce them.

Technically, a human approves the whole lot earlier than any motion is taken. The human is within the course of. However the course of is now transferring so quick that it turns into inconceivable to guage what’s in it in quick paced eventualities like a navy assault.When Claude generates an intelligence abstract, that abstract turns into the enter for the subsequent determination. And since Claude can produce these summaries a lot quicker than people can course of them, the tempo of all the operation quickens.

You’ll be able to’t decelerate to consider carefully a couple of advice when the scenario it describes is already three minutes outdated. The data has moved on. The following replace is already arriving. The loop retains getting quicker.

90 seconds to determine. that is what the loop seems like from inside.

The requirement for human approval is there however the potential to meaningfully consider what you are approving just isn’t.

And it will get structurally worse the higher the AI will get as a result of higher AI means quicker synthesis, shorter determination home windows, much less time to assume earlier than appearing.

Pentagon and Claude’s arguments

The Pentagon desires entry to AI fashions for any use case that complies with U.S. regulation. Their place is basically: utilization coverage is our drawback, not yours.

However Anthropic desires to take care of particular prohibitions – no totally autonomous weapons and prohibiting mass home surveillance of People.

After the WSJ broke the story, a senior administration official informed Axios their partnership/settlement was underneath evaluate and that is the rationale Pentagon said:

“Any firm that might jeopardize the operational success of our warfighters within the subject is one we have to reevaluate.”

However satirically, Anthropic is at present the one business AI mannequin authorized for sure labeled DoD networks. Though, OpenAI, Google, and xAI are all actively in discussions to get onto these methods with fewer restrictions.

The true struggle past arguments

In hindsight, Anthropic and the Pentagon is likely to be lacking all the level and pondering coverage languages would possibly resolve this problem.

Contracts can mandate human approval at each step. However, that doesn’t imply the human has sufficient time, context, or cognitive bandwidth to truly consider what they’re approving. That hole between a human technically within the loop and a human really capable of assume clearly about what’s in it’s the place the actual threat lives.

Rogue AI and autonomous weapons are most likely the later set of arguments.

In the present day’s debate must be – would you name it “supervised” if you put a system that processes info orders of magnitude quicker than people right into a human command chain?

Closing ideas

In Caracas, in January, with 150 plane and real-time feeds and selections being made at operational pace and we do not know the reply to that.

And neither does Anthropic.

However quickly, with fewer restrictions in place and extra fashions on these labeled networks, we’re all going to seek out out.

All claims on this piece are sourced to public reporting and documented specs. We’ve no private details about this operation. Sources: WSJ (Feb 13), Axios (Feb 13, Feb 15), Reuters (Jan 3, Feb 13). Casualty figures from Cuba’s official authorities assertion and Venezuela’s protection ministry. API structure from platform.claude.com/docs. Contract particulars from Anthropic’s August 2025 press launch. “Visibility into utilization” quote from Axios (Feb 13).

Apple’s low-cost MacBook rumors: Specs, value & launch date for A19 Mac

Technology

Dr. Mike

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February 19, 2026

0

Apple’s low-cost MacBook rumors: Specs, value & launch date for A19 Mac

Abstract created by Good Solutions AI

In abstract:

Macworld studies Apple is growing a price range MacBook utilizing an A18 Professional or A19 Professional chip, anticipated to launch by early 2026 with pricing between $599-$849.
This inexpensive laptop computer goals to compete with Chromebooks whereas sustaining premium aluminum design and providing playful colours like yellow, inexperienced, and blue.
The gadget could characteristic a 12.9-inch Retina show, 8GB RAM for Apple Intelligence, and superior connectivity together with Wi-Fi 7 and potential mobile help.

Apple’s MacBook lineup could possibly be in for a serious shakeup. The corporate is reportedly planning on releasing a brand new, low-cost laptop computer that may compete with Chromebooks and Home windows laptops within the price range market. Such a laptop computer might carry the Mac to an entire new class of customers.

The brand new MacBook could characteristic an aluminum design in playful colours, an A19 Professional chip, Extremely Retina XDR OLED show, fewer ports, and Apple’s C2 modem.

This new, low-cost MacBook could possibly be a highly regarded one, which implies you’ll wish to maintain monitor of its developments. Nicely, look no additional. This text will maintain monitor of the rumors and studies of this upcoming product, in addition to supply our perspective on the gadget, so control this web page for the most recent.

New A19 MacBook: Launch date

March 4, 2026 – potential

Apple is scheduled to host three “Apple Particular Experiences” on March 4 within the U.S., U.Okay., and China. These periods, that are anticipated to supply media hands-on time with new merchandise, might present the perfect setting for the price range MacBook’s debut.

Apple’s low-cost MacBook has been rumored for a while, with Apple leaker Jon Prosser describing a brand new colourful MacBook Air again in Could 2021, that by no means materialised, however over the previous yr rumors have remerged suggesting that Apple does have one thing completely different coming.

In June 2025, credible analyst Ming-Chi Kuo, indicated (in Chinese language) that Apple would begin manufacturing of the brand new low-cost MacBook on the finish of the fourth quarter of 2025 or within the early stage of the primary quarter of 2026. It’s value emphasising that these dates consult with the manufacturing course of reasonably than the announcement or delivery timeframe. If manufacturing started in October 2025 then it’s believable that the price range MacBook will arrive in early 2026, doubtlessly unveiled at a spring occasion, or within the run as much as Apple’s March 4 ‘Expertise’.

In November, 2025, Bloomberg’s Mark Gurman reported that Apple is planning to launch a “low-cost laptop computer to rival Chromebooks and Home windows PCs” within the first half of 2026. He stated it was at present in testing at Apple and “in early manufacturing with abroad suppliers.”

On February 8, 2026, Gurman reported that the low-cost MacBook shall be accessible “within the first half of the yr.” He didn’t supply any specifics. After which, on February 15, he wrote that Apple’s long-rumored low-cost MacBook is anticipated to reach “as early as March,” with a design that defies its price ticket.

What is going to the brand new MacBook be known as?

No studies have been made in regards to the official identify of the laptop computer, however you’ll be able to assume it received’t be a part of the MacBook Air line. It’s being known as the A18 or A19 Professional MacBook, primarily based on the chip it might have. Apple might merely name it “MacBook,” reviving the identify of the discontinued 12-inch MacBook that was focused at the same market.

Foundry

New A19 MacBook: Design

Playful colours
Aluminum chassis (not plastic)

Based on Mark Gurman (in February 2026), Apple’s low-cost MacBook can have a design that defies its price ticket.

Apple’s purpose with this laptop computer is to make it inexpensive in comparison with the MacBook Air and MacBook Professional, and one solution to reduce prices is to make a plastic case as a substitute of an aluminum one. Nonetheless, since Apple is working to make its merchandise carbon-neutral, it’s uncertain that it could use plastic, preferring to make use of aluminum or another recyclable or carbon-free materials. Due to this fact, it’s no shock that the brand new MacBook, in accordance with Gurman, can have an aluminum chassis.

There shall be a brand new manufacturing course of “designed to be each quicker and less expensive than the one used with Apple’s present laptops,” in accordance with Gurman.

Together with the aluminum shell, Gurman studies that the brand new MacBook shall be accessible in “playful colours.”

The next colors are indicated: yellow, mild inexperienced, blue, silver, darkish grey, and pink. Which, within the case of yellow, inexperienced and blue, does appear to match the brand Apple designed for the invitations to the particular occasions on March 4.

Apple

Again in June 2023 analyst Ming-Chi Kuo advised the next colours: silver, blue, pink, and yellow.

A19 Professional MacBook: Show

12.9-inch show – rumored

Apple at present makes use of a Liquid Retina (LED-backlit show with IPS know-how) show within the MacBook Air, an improve over the unique Retina shows Apple final used with the 13-inch MacBook Professional in 2022. It’s potential Apple might revert to a Retina show with squared-off corners for the A19 Professional MacBook, although it’s extra doubtless that Apple shaves price through the use of a smaller show.

On August 11, DigiTimes reported that the laptop computer can have a 12.9-inch show, barely smaller than the 13-inch MacBook Air.

New A19 MacBook: Processor

iPhone chip: A19 Professional or A18 Professional – anticipated

One other solution to meet the pricing targets for this new MacBook is to make use of a chip that isn’t as sturdy because the chips used within the MacBook Air and MacBook Professional. When Kuo first mentioned the price range MacBook in his June 2025 report, he said that Apple will use an A18 Professional, the identical chip that was within the iPhone 16 Professional. Now that the A19 arrived with the iPhone 17 that is most likely the extra doubtless chip. Learn extra about what the iPhone 17 can inform us in regards to the MacBook that would use the identical chip in: Is Apple’s rumored low cost MacBook hiding in plain sight?

The A-series chips are utilized in iPhones, whereas M-series chips are in Macs. Each chip households are primarily based on the identical normal structure, however the M chips typically supply extra processing energy.

An A19 Professional (and A18 Professional) can technically do the whole lot an M-series chip can, however will probably be slower. When evaluating Geekbench 6 benchmarks, the A18 Professional provided single-core efficiency that’s on par with the M3. Nonetheless, as a result of the A18 Professional has solely 6 CPU cores, its multi-core efficiency is barely behind the M1, which has 8 CPU cores. If Apple retains the whole lot the identical, an A18 Professional MacBook will carry out like an M1 MacBook Air, which, for lots of people, has greater than sufficient processing energy. Ought to Apple use an A19 Professional we will count on one thing even higher.

It should draw comparability with the iPad fashions which can be powered by Mac chips, although.

MacRumors reported, in December 2025, that references to an A18 Professional MacBook appeared in a set of leaked Apple recordsdata.

New A19 MacBook: Connectivity

Apple is utilizing its personal wi-fi networking chip, the N1 within the iPhone 17, bringing Bluetooth 6, Wi-Fi 7, and Thread. It’s doubtless the identical chips will make their method into the A19 Professional MacBook.

There’s additionally an opportunity this new Mac can have mobile connectivity utilizing Apple’s C1, C1X or but to launch C2 modem. The C2 is more likely to help mmWave.

Except for this, it’s rumored that the price range MacBook can have fewer USB ports than the MacBook Air (which has two USB-C and a magsafe charging port). This low-cost MacBook could not help Thunderbolt.

New A19 MacBook: Digital camera

It’s additionally potential that Apple opts to not embody a built-in digital camera. Over the previous a number of years, Apple has promoted the Mac’s Continuity Digital camera characteristic that allows you to use an iPhone as a Mac webcam, in addition to the upcoming macOS Tahoe Magnifier app. Apple might promote this as a webcam answer. This could be a drastic change, because it’s been a very long time since Apple shipped a laptop computer and not using a digital camera, however it could possibly be a chance with a view to meet the corporate’s value purpose.

New A19 MacBook: Efficiency

^{Outcomes are expressed as Geekbench scores. Greater scores/longer bars are quicker.}

We don’t but have the benchmarks for the A19 Professional, however the A18 Professional has 8GB of RAM, which Apple has stated is the naked minimal required to run Apple Intelligence options. So, this new laptop computer can have no less than 8GB of RAM and probably 16GB, like the opposite MacBook fashions. For the reason that iPhone doesn’t supply RAM upgrades, we don’t know what Apple might cost for additional reminiscence.

As for graphics efficiency, the A18 Professional has a 6-core GPU, and its Metallic rating in Geekbench 6’s Compute benchmark is much like that of the M1 discovered within the MacBook Air. It’s not a cutting-edge performer, however it’ll deal with on a regular basis duties with none points. Since Apple’s different MacBooks all begin at 256GB of storage, it’s almost sure this mannequin will include 256GB of storage as properly.

New A19 MacBook: Value

The most affordable laptop computer Apple sells is the $999 MacBook Air. Nonetheless, Walmart nonetheless sells the M1 MacBook Air with a 256GB SSD for $649 and as little as $599. The A19 Professional MacBook has been regarded as a substitute for this Walmart mannequin, although it’s laborious to consider it could be that low cost. A value that falls between $729 and $849 looks like it could match into the MacBook lineup.

There are claims that the low-cost MacBook might begin at $599, which might permit Apple to compete with Chromebooks and Home windows laptops.

Gurman studies that the brand new laptop computer will price “properly underneath $1,000.”

Our estimation could be across the center of these two figures, round $749.

Microbe with the smallest genome but pushes the boundaries of life

Science

Dr. Mike

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February 19, 2026

0

Microbe with the smallest genome but pushes the boundaries of life

Symbiotic micro organism reside inside specialised organs known as bacteriomes inside bugs. This picture exhibits a cross-section of the planthopper Callodictya krueperi, with fluorescent probes labelling three microbes: Vidania (purple), Sodalis (yellow) and Sulcia (inexperienced)

Courtesy Anna Michalik et al

Symbiotic micro organism residing inside insect cells have the smallest genomes identified for any organism. The findings additional muddy the excellence between mobile organelles like mitochondria and essentially the most barebones microbes in nature.

“Precisely the place this extremely built-in symbiont ends and an organelle begins, I believe it’s very tough to say,” says Piotr Łukasik at Jagiellonian College in Kraków, Poland. “This can be a very blurred boundary.”

Planthoppers are bugs that subsist fully on plant sap, and complement their vitamin because of an historic relationship with symbiotic micro organism. Over many hundreds of thousands of years, these microbes advanced to reside inside specialised cells within the planthoppers’ abdomens, producing vitamins that the planthoppers can’t get from their sugary weight loss plan. Many of those micro organism are completely depending on their hosts and have let their genetic toolkits deteriorate to a fraction of their ancestral dimension.

Łukasik and his colleagues had been within the evolution of this bacteria-bug relationship and simply how small these bacterial genomes might get. The staff sampled 149 particular person bugs throughout 19 planthopper households, extracting DNA from the bugs’ stomach tissues. The researchers analysed and sequenced the DNA, reconstructing the genomes of the symbiotic micro organism Vidania and Sulcia.

The bacterial genomes had been exceptionally tiny. Genome size could be measured in numbers of base pairs, the sequence of paired “letters” in genetic code. The bacterial genomes had been lower than 181,000 base pairs lengthy. For comparability, the human genome is billions of base pairs lengthy.

A few of the Vidania genomes had been simply 50,000 base pairs lengthy, the smallest identified for any life kind. Beforehand, the smallest was Nasuia, a symbiotic bacterium hosted by planthopper relations known as leafhoppers, measuring simply over 100,000 base pairs.

At 50,000 base pairs, the Vidania genomes are on the dimensions of these present in viruses, which aren’t thought of to be alive: the virus behind covid-19 has a genome round 30,000 base pairs lengthy, as an example. A few of the Vidania have solely about 60 protein-coding genes, among the many lowest counts on report.

Planthoppers depend on symbiotic micro organism to complement their specialised diets

Courtesy Anna Michalik et al

The micro organism have been evolving with their insect hosts for about 263 million years, independently evolving extraordinarily small genome sizes inside two completely different teams of planthoppers. One of many few issues these micro organism do is produce the amino acid phenylalanine, which is a chemical precursor for making and strengthening insect exoskeletons.

Łukasik and his staff assume that the huge lack of genes would possibly occur when the bugs eat new meals with vitamins that was equipped by the micro organism, or when extra microbes transfer in and take over these roles.

The extremely decreased micro organism are harking back to mitochondria and chloroplasts – energy-producing organelles inside animal and plant cells descended from historic micro organism. The symbiotic micro organism equally reside throughout the host cells and are handed down between generations.

“‘Organelle’ is only a phrase, so it’s high-quality with me to name these organelles if somebody desires to incorporate these within the definition,” says Nancy Moran on the College of Texas at Austin, who was not concerned with the analysis. “However there stay variations from mitochondria or chloroplasts.”

Mitochondria are a lot older, having arisen 1.5 billion years in the past or extra, and their genomes are smaller nonetheless – about 15,000 base pairs.

“These symbionts reside solely in specialised host cells, not in most cells all through the organism, as seen in mitochondria and chloroplasts,” says Moran.

Łukasik considers these micro organism and mitochondria as merely being at completely different locations on an evolutionary “gradient of dependence” on their hosts. He suspects even tinier symbiote genomes have but to be found.

Subjects:

Construct unified intelligence with Amazon Bedrock AgentCore

Machine Learning

Dr. Mike

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February 19, 2026

0

Construct unified intelligence with Amazon Bedrock AgentCore

Constructing cohesive and unified buyer intelligence throughout your group begins with decreasing the friction your gross sales representatives face when toggling between Salesforce, assist tickets, and Amazon Redshift. A gross sales consultant making ready for a buyer assembly would possibly spend hours clicking by way of a number of totally different dashboards—product suggestions, engagement metrics, income analytics, and so on. – earlier than creating an entire image of the shopper’s scenario. At AWS, our gross sales group skilled this firsthand as we scaled globally. We would have liked a approach to unify siloed buyer information throughout metrics databases, doc repositories, and exterior business sources – with out constructing advanced customized orchestration infrastructure.

We constructed the Buyer Agent & Data Engine (CAKE), a buyer centric chat agent utilizing Amazon Bedrock AgentCore to unravel this problem. CAKE coordinates specialised retriever instruments – querying information graphs in Amazon Neptune, metrics in Amazon DynamoDB, paperwork in Amazon OpenSearch Service, and exterior market information utilizing an internet search API, together with safety enforcement utilizing Row Degree Safety device (RLS), delivering buyer insights by way of pure language queries in underneath 10 seconds (as noticed in agent load assessments).

On this submit, we display how one can construct unified intelligence techniques utilizing Amazon Bedrock AgentCore by way of our real-world implementation of CAKE. You’ll be able to construct customized brokers that unlock the next options and advantages:

Coordination of specialised instruments by way of dynamic intent evaluation and parallel execution
Integration of purpose-built information shops (Neptune, DynamoDB, OpenSearch Service) with parallel orchestration
Implementation of row-level safety and governance inside workflows
Manufacturing engineering practices for reliability, together with template-based reporting to stick to enterprise semantic and magnificence
Efficiency optimization by way of mannequin flexibility

These architectural patterns can assist you speed up improvement for various use circumstances, together with buyer intelligence techniques, enterprise AI assistants, or multi-agent techniques that coordinate throughout totally different information sources.

Why buyer intelligence techniques want unification

As gross sales organizations scale globally, they usually face three crucial challenges: fragmented information throughout specialised instruments (product suggestions, engagement dashboards, income analytics, and so on.) requiring hours to collect complete buyer views, lack of enterprise semantics in conventional databases that may’t seize semantic relationships explaining why metrics matter, and guide consolidation processes that may’t scale with rising information volumes. You want a unified system that may combination buyer information, perceive semantic relationships, and motive by way of buyer wants in enterprise context, making CAKE the important linchpin for enterprises in all places.

Answer overview

CAKE is a customer-centric chat agent that transforms fragmented information into unified, actionable intelligence. By consolidating inner and exterior information sources/tables right into a single conversational endpoint, CAKE delivers customized buyer insights powered by context-rich information graphs—all in underneath 10 seconds. In contrast to conventional instruments that merely report numbers, the semantic basis of CAKE captures the that means and relationships between enterprise metrics, buyer behaviors, business dynamics, and strategic contexts. This permits CAKE to clarify not simply what is occurring with a buyer, however why it’s occurring and how one can act.

Amazon Bedrock AgentCore gives the runtime infrastructure that multi-agent AI techniques require as a managed service, together with inter-agent communication, parallel execution, dialog state monitoring, and power routing. This helps groups give attention to defining agent behaviors and enterprise logic reasonably than implementing distributed techniques infrastructure.

For CAKE, we constructed a customized agent on Amazon Bedrock AgentCore that coordinates 5 specialised instruments, every optimized for various information entry patterns:

Neptune retriever device for graph relationship queries
DynamoDB agent for fast metric lookups
OpenSearch retriever device for semantic doc search
Net search device for exterior business intelligence
Row degree safety (RLS) device for safety enforcement

The next diagram exhibits how Amazon Bedrock AgentCore helps the orchestration of those elements.

The answer flows by way of a number of key phases in response to a query (for instance, “What are the highest enlargement alternatives for this buyer?”):

Analyzes intent and routes the question – The supervisor agent, working on Amazon Bedrock AgentCore, analyzes the pure language question to find out its intent. The query requires buyer understanding, relationship information, utilization metrics, and strategic insights. The agent’s tool-calling logic, utilizing Amazon Bedrock AgentCore Runtime, identifies which specialised instruments to activate.
Dispatches instruments in parallel – Moderately than executing device calls sequentially, the orchestration layer dispatches a number of retriever instruments in parallel, utilizing the scalable execution setting of Amazon Bedrock AgentCore Runtime. The agent manages the execution lifecycle, dealing with timeouts, retries, and error circumstances routinely.
Synthesizes a number of outcomes – As specialised instruments return outcomes, Amazon Bedrock AgentCore streams these partial responses to the supervisor agent, which synthesizes them right into a coherent reply. The agent causes about how totally different information sources relate to one another, identifies patterns, and generates insights that span a number of information domains.
Enforces safety boundaries – Earlier than information retrieval begins, the agent invokes the RLS device to deterministically implement consumer permissions. The customized agent then verifies that subsequent device calls respect these safety boundaries, routinely filtering outcomes and serving to forestall unauthorized information entry. This safety layer operates on the infrastructure degree, decreasing the danger of implementation errors.

This structure operates on two parallel tracks: Amazon Bedrock AgentCore gives the runtime for the real-time serving layer that responds to consumer queries with minimal latency, and an offline information pipeline periodically refreshes the underlying information shops from the analytical information warehouse. Within the following sections, we talk about the agent framework design and core answer elements, together with the information graph, information shops, and information pipeline.

Agent framework design

Our multi-agent system leverages the AWS Strands Brokers framework to ship structured reasoning capabilities whereas sustaining the enterprise controls required for regulatory compliance and predictable efficiency. The multi-agent system is constructed on the AWS Strands Brokers framework, which gives a model-driven basis for constructing brokers from many alternative fashions. The supervisor agent analyzes incoming inquiries to intelligently choose which specialised brokers and instruments to invoke and how one can decompose consumer queries. The framework exposes agent states and outputs to implement decentralized analysis at each agent and supervisor ranges. Constructing on model-driven strategy, we implement agentic reasoning by way of GraphRAG reasoning chains that assemble deterministic inference paths by traversing information relationships. Our brokers carry out autonomous reasoning inside their specialised domains, grounded round pre-defined ontologies whereas sustaining predictable, auditable habits patterns required for enterprise functions.

The supervisor agent employs a multi-phase choice protocol:

Query evaluation – Parse and perceive consumer intent
Supply choice – Clever routing determines which mixture of instruments are wanted
Question decomposition – Authentic questions are damaged down into specialised sub-questions optimized for every chosen device
Parallel execution – Chosen instruments execute concurrently by way of serverless AWS Lambda motion teams

Instruments are uncovered by way of a hierarchical composition sample (accounting for information modality—structured vs. unstructured) the place high-level brokers and instruments coordinate a number of specialised sub-tools:

Graph reasoning device – Manages entity traversal, relationship evaluation, and information extraction
Buyer insights agent – Coordinates a number of fine-tuned fashions in parallel for producing buyer summaries from tables
Semantic search device – Orchestrates unstructured textual content evaluation (comparable to subject notes)
Net analysis device – Coordinates internet/information retrieval

We prolong the core AWS Strands Brokers framework with enterprise-grade capabilities together with buyer entry validation, token optimization, multi-hop LLM choice for mannequin throttling resilience, and structured GraphRAG reasoning chains. These extensions ship the autonomous decision-making capabilities of contemporary agentic techniques whereas facilitating predictable efficiency and regulatory compliance alignment.

Constructing the information graph basis

CAKE’s information graph in Neptune represents buyer relationships, product utilization patterns, and business dynamics in a structured format that empowers AI brokers to carry out environment friendly reasoning. In contrast to conventional databases that retailer info in isolation, CAKE’s information graph captures the semantic that means of enterprise entities and their relationships.

Graph building and entity modeling

We designed the information graph round AWS gross sales ontology—the core entities and relationships that gross sales groups talk about every day:

Buyer entities – With properties extracted from information sources together with business classifications, income metrics, cloud adoption section, and engagement scores
Product entities – Representing AWS companies, with connections to make use of circumstances, business functions, and buyer adoption patterns
Answer entities – Linking merchandise to enterprise outcomes and strategic initiatives
Alternative entities – Monitoring gross sales pipeline, deal levels, and related stakeholders
Contact entities – Mapping relationship networks inside buyer organizations

Amazon Neptune excels at answering questions that require understanding connections—discovering how two entities are associated, figuring out paths between accounts, or discovering oblique relationships that span a number of hops. The offline information building course of runs scheduled queries towards Redshift clusters to organize information to be loaded within the graph.

Capturing relationship context

CAKE’s information graph captures how relationships join entities. When the graph connects a buyer to a product by way of an elevated utilization relationship, it additionally shops contextual attributes: the speed of improve, the enterprise driver (from account plans), and associated product adoption patterns. This contextual richness helps the LLM perceive enterprise context and supply explanations grounded in precise relationships reasonably than statistical correlation alone.

Goal-built information shops

Moderately than storing information in a single database, CAKE makes use of specialised information shops, every designed for the way it will get queried. Our customized agent, working on Amazon Bedrock AgentCore, manages the coordination throughout these shops—sending queries to the correct database, working them on the identical time, and mixing outcomes—so each customers and builders work with what seems like a single information supply:

Neptune for graph relationships – Neptune shops the net of connections between prospects, accounts, stakeholders, and organizational entities. Neptune excels at multi-hop traversal queries that require costly joins in relational databases—discovering relationship paths between disconnected accounts, or discovering prospects in an business who’ve adopted particular AWS companies. When Amazon Bedrock AgentCore identifies a question requiring relationship reasoning, it routinely routes to the Neptune retriever device.
DynamoDB for fast metrics – DynamoDB operates as a key-value retailer for precomputed aggregations. Moderately than computing buyer well being scores or engagement metrics on-demand, the offline pipeline pre-computes these values and shops them listed by buyer ID. DynamoDB then delivers sub-10ms lookups, enabling on the spot report technology. Instrument chaining in Amazon Bedrock AgentCore permits it to retrieve metrics from DynamoDB, move them to the magnifAI agent (our customized table-to-text agent) for formatting, and return polished experiences—all with out customized integration code.
OpenSearch Service for semantic doc search – OpenSearch Service shops unstructured content material like account plans and subject notes. Utilizing embedding fashions, OpenSearch Service converts textual content into vector representations that assist semantic matching. When Amazon Bedrock AgentCore receives a question about “digital transformation,” for instance, it acknowledges the necessity for semantic search and routinely routes to the OpenSearch Service retriever device, which finds related passages even when paperwork use totally different terminology.
S3 for doc storage – Amazon Easy Storage Service (Amazon S3) gives the muse for OpenSearch Service. Account plans are saved as Parquet information in Amazon S3 earlier than being listed as a result of the supply warehouse (Amazon Redshift) has truncation limits that may reduce off massive paperwork. This multi-step course of—Amazon S3 storage, embedding technology, OpenSearch Service indexing—preserves full content material whereas sustaining the low latency required for real-time queries.

Constructing on Amazon Bedrock AgentCore makes these multi-database queries really feel like a single, unified information supply. When a question requires buyer relationships from Neptune, metrics from DynamoDB, and doc context from OpenSearch Service, our agent routinely dispatches requests to all three in parallel, manages their execution, and synthesizes their outcomes right into a single coherent response.

Information pipeline and steady refresh

The CAKE offline information pipeline operates as a batch course of that runs on a scheduled cadence to maintain the serving layer synchronized with the most recent enterprise information. The pipeline structure separates information building from information serving, so the real-time question layer can keep low latency whereas the batch pipeline handles computationally intensive aggregations and graph building.

The Information Processing Orchestration layer coordinates transformations throughout a number of goal databases. For every database, the pipeline performs the next steps:

Extracts related information from Amazon Redshift utilizing optimized queries
Applies enterprise logic transformations particular to every information retailer’s necessities
Hundreds processed information into the goal database with acceptable indexes and partitioning

For Neptune, this entails extracting entity information, establishing graph nodes and edges with property attributes, and loading the graph construction with semantic relationship sorts. For DynamoDB, the pipeline computes aggregations and metrics, buildings information as key-value pairs optimized for buyer ID lookups, and applies atomic updates to take care of consistency. For OpenSearch Service, the pipeline follows a specialised path: massive paperwork are first exported from Amazon Redshift to Amazon S3 as Parquet information, then processed by way of embedding fashions to generate vector representations, that are lastly loaded into the OpenSearch Service index with acceptable metadata for filtering and retrieval.

Engineering for manufacturing: Reliability and accuracy

When transitioning CAKE from prototype to manufacturing, we carried out a number of crucial engineering practices to facilitate reliability, accuracy, and belief in AI-generated insights.

Mannequin flexibility

The Amazon Bedrock AgentCore structure decouples the orchestration layer from the underlying LLM, permitting versatile mannequin choice. We carried out mannequin hopping to supply automated fallback to different fashions when throttling happens. This resilience occurs transparently inside AgentCore’s Runtime—detecting throttling circumstances, routing requests to obtainable fashions, and sustaining response high quality with out user-visible degradation.

Row-Degree Safety (RLS) and Information Governance

Earlier than information retrieval happens, the RLS device enforces row-level safety based mostly on consumer identification and organizational hierarchy. This safety layer operates transparently to customers whereas sustaining strict information governance:

Gross sales representatives entry solely prospects assigned to their territories
Regional managers view aggregated information throughout their areas
Executives have broader visibility aligned with their obligations

The RLS device routes queries to acceptable information partitions and applies filters on the database question degree, so safety could be enforced within the information layer reasonably than counting on application-level filtering.

Outcomes and influence

CAKE has remodeled how AWS gross sales groups entry and act on buyer intelligence. By offering on the spot entry to unified insights by way of pure language queries, CAKE reduces the time spent looking for info from hours to seconds as per surveys/suggestions from customers, serving to gross sales representatives give attention to strategic buyer engagement reasonably than information gathering.

The multi-agent structure delivers question responses in seconds for many queries, with the parallel execution mannequin supporting simultaneous information retrieval from a number of sources. The information graph allows refined reasoning that goes past easy information aggregation—CAKE explains why tendencies happen, identifies patterns throughout seemingly unrelated information factors, and generates suggestions grounded in enterprise relationships. Maybe most significantly, CAKE democratizes entry to buyer intelligence throughout the group. Gross sales representatives, account managers, options architects, and executives work together with the identical unified system, offering constant buyer insights whereas sustaining acceptable safety and entry controls.

Conclusion

On this submit, we confirmed how Amazon Bedrock AgentCore helps CAKE’s multi-agent structure. Constructing multi-agent AI techniques historically requires vital infrastructure funding, together with implementing customized agent coordination protocols, managing parallel execution frameworks, monitoring dialog state, dealing with failure modes, and constructing safety enforcement layers. Amazon Bedrock AgentCore reduces this undifferentiated heavy lifting by offering these capabilities as managed companies inside Amazon Bedrock.

Amazon Bedrock AgentCore gives the runtime infrastructure for orchestration, and specialised information shops excel at their particular entry patterns. Neptune handles relationship traversal, DynamoDB gives on the spot metric lookups, and OpenSearch Service helps semantic doc search, however our customized agent, constructed on Amazon Bedrock AgentCore, coordinates these elements, routinely routing queries to the correct instruments, executing them in parallel, synthesizing their outcomes, and sustaining safety boundaries all through the workflow. The CAKE expertise demonstrates how Amazon Bedrock AgentCore can assist groups construct multi-agent AI techniques, rushing up the method from months of infrastructure improvement to weeks of enterprise logic implementation. By offering orchestration infrastructure as a managed service, Amazon Bedrock AgentCore helps groups give attention to area experience and buyer worth reasonably than constructing distributed techniques infrastructure from scratch.

To be taught extra about Amazon Bedrock AgentCore and constructing multi-agent AI techniques, check with the Amazon Bedrock Consumer Information, Amazon Bedrock Workshop, and Amazon Bedrock Brokers. For the most recent information on AWS, see What’s New with AWS.

Acknowledgments

We prolong our honest gratitude to our government sponsors and mentors whose imaginative and prescient and steerage made this initiative doable: Aizaz Manzar, Director of AWS International Gross sales; Ali Imam, Head of Startup Phase; and Akhand Singh, Head of Information Engineering.

We additionally thank the devoted group members whose technical experience and contributions have been instrumental in bringing this product to life: Aswin Palliyali Venugopalan, Software program Dev Supervisor; Alok Singh, Senior Software program Growth Engineer; Muruga Manoj Gnanakrishnan, Principal Information Engineer; Sai Meka, Machine Studying Engineer; Invoice Tran, Information Engineer; and Rui Li, Utilized Scientist.

Concerning the authors

Monica Jain is a Senior Technical Product Supervisor at AWS International Gross sales and an analytics skilled driving AI-powered gross sales intelligence at scale. She leads the event of generative AI and ML-powered information merchandise—together with information graphs, AI-augmented analytics, pure language question techniques, and suggestion engines, that enhance vendor productiveness and decision-making. Her work allows AWS executives and sellers worldwide to entry real-time insights and speed up data-driven buyer engagement and income development.

M. Umar Javed is a Senior Utilized Scientist at AWS, with over 8 years of expertise throughout academia and business and a PhD in ML principle. At AWS, he builds production-grade generative AI and machine studying options, with work spanning multi-agent LLM architectures, analysis on small language fashions, information graphs, suggestion techniques, reinforcement studying, and multi-modal deep studying. Previous to AWS, Umar contributed to ML analysis at NREL, CISCO, Oxford, and UCSD. He’s a recipient of the ECEE Excellence Award (2021) and contributed to 2 Donald P. Eckman Awards (2021, 2023).

Damien Forthomme is a Senior Utilized Scientist at AWS, main a Information Science group in AWS Gross sales, Advertising, and International Providers (SMGS). With greater than 10 years of expertise and a PhD in Physics, he focuses on utilizing and constructing superior machine studying and generative AI instruments to floor the correct information to the correct individuals on the proper time. His work encompasses initiatives comparable to forecasting, suggestion techniques, core foundational datasets creation, and constructing generative AI merchandise that improve gross sales productiveness for the group.

Mihir Gadgil is a Senior Information Engineer in AWS Gross sales, Advertising, and International Providers (SMGS), specializing in enterprise-scale information options and generative AI functions. With over 9 years of expertise and a Grasp’s in Data Know-how & Administration, he focuses on constructing strong information pipelines, advanced information modeling, and ETL/ELT processes. His experience drives enterprise transformation by way of modern information engineering options and superior analytics capabilities.

Sujit Narapareddy, Head of Information & Analytics at AWS International Gross sales, is a know-how chief driving international enterprise transformation. He leads information product and platform groups that energy the AWS’s Go-to-Market by way of AI-augmented analytics and clever automation. With a confirmed observe document in enterprise options, he has remodeled gross sales productiveness, information governance, and operational excellence. Beforehand at JPMorgan Chase Enterprise Banking, he formed next-generation FinTech capabilities by way of information innovation.

Norman Braddock, Senior Supervisor of AI Product Administration at AWS, is a product chief driving the transformation of enterprise intelligence by way of agentic AI. He leads the Analytics & Insights Product Administration group inside Gross sales, Advertising, and International Providers (SMGS), delivering merchandise that bridge AI mannequin efficiency with measurable enterprise influence. With a background spanning procurement, manufacturing, and gross sales operations, he combines deep operational experience with product innovation to form the way forward for autonomous enterprise administration.

Android is getting a brand new note-taking app in April 2026, and I could not be extra excited

Technology

Dr. Mike

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February 19, 2026

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It is no secret that app builders appear to pay extra consideration to Apple platforms than Android or Home windows. The checklist of iPhone-only apps that I lengthy for on Android is brief, however notable. If I needed to slim it down to only two iOS apps I might love to make use of on my Android units, note-taking app Notability and journey app Flighty can be on the high of my checklist. It should get shorter in April, as a result of Notability is lastly getting an Android model.

Notability received a significant improve simply final week that helped it inch nearer to changing into a very cross-platform notes app. It gained an online shopper, that means you could entry Notability notes on any machine with a browser, together with on Android telephones. The net shopper helps each staple Notability function, reminiscent of stay recordings and transcripts, file uploads and modifying, and markup instruments. Utilizing the Notability Cloud sync perform, notes created within the iOS, iPadOS, or macOS apps shall be accessible on the net shopper, and vice versa.

It is the closest factor to true Android help Notability has ever had, however a real Google Play Retailer model is coming quickly. The corporate confirmed in a press launch final week that Notability will attain Android beta testers in April 2026, offering an early have a look at the app’s consumer interface and options. Crucially, the upcoming app is not simply an iOS clone — it can have Android-specific options, like lock display word shortcuts.

Google Preserve and Samsung Notes to be far too restricted for my wants, particularly on tablets. It is tremendous for typing out a fleeting thought in your Android telephone or sustaining a grocery checklist, however something greater than that can make you would like you used a fully-featured notes apps. On the opposite finish of the spectrum are apps like Notion, that are too highly effective and complicated for my use circumstances. I’ve at all times thought Notability struck a terrific stability between these two extremes.

I first bought Notability a decade in the past, and it helped me get via my highschool and school research. Since then, I’ve used Notability to take notes throughout press conferences and interviews. Sadly, the one-time buy possibility I used to purchase Notability within the first place is not accessible. Notability now makes use of a subscription mannequin, nevertheless it additionally added a free model for the primary time as a part of the swap.

An early look at the Notability for Android user interface.

An early have a look at the Notability for Android consumer interface. (Picture credit score: Notability)

Notability’s greatest function is audio recording, transcription, and sync. When handwriting or typing in Notability, you’ll be able to report audio alongside the notes you take. The audio is synced to your actions within the Notability app. While you playback the audio recording, you’ll be able to faucet a phrase or drawing in your digital pocket book and listen to precisely what was mentioned once you wrote it. This turns out to be useful when revisiting notes from conferences or lectures, as you’ll be able to rehear what the speaker or professor mentioned within the second to refresh your reminiscence.

One other worthy function is form and handwriting recognition, which is fairly self explanatory. While you draw a form, the Notability software program can acknowledge and snap it into excellent type. If in case you have horrible sketching abilities or poor handwriting like me, this turns out to be useful. The app can even acknowledge your handwriting and convert it into textual content whereas providing full help for a stylus, mouse, and keyboard. There’s doc import help, which I exploit as an alternative of different markup instruments like Adobe Acrobat.

app’s web site. The app will help all of the aforementioned options whereas in beta, however extra are on the way in which. Notability is teasing a common launch with “new AI-powered options via Notability Study, notes export, help for the Notability Gallery, a phone-optimized model, and extra superior drawing instruments.”

Should you love productiveness apps or note-taking instruments, it is value signing up for the Notability for Android beta. In order for you an early have a look at the expertise, you’ll be able to attempt the Notability net shopper totally free as we speak. It is the most effective iOS, iPadOS, and macOS apps ever made, and it is nice that Android customers will quickly get to attempt it.