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TL;DR: The idea is to use a set of ordinary JPEG images as a distributed medium for an encrypted container. The data is split into redundant fragments and distributed across the images, allowing the container to be recovered even if some of the JPEG files are lost. We will not examine the code; instead, we will focus only on the general principle behind the approach. Where the idea came from Most steganographic systems follow a "one image — one file" model. If the image is lost or damaged, the embedded data is lost with it. The idea was to distribute the data container across multiple JPEG images while preserving the ability to recover the data even if some of those images are lost. I should point out right away that this is not steganography in the traditional sense. The additional data is written to JPEG images after the EOI ( End Of Image ) marker, where it can be easily detected by even the simplest analysis. The goal is not to completely hide the presence of the data, but to use ordinary JPEG images as a distributed medium for an encrypted container. At the same time, in everyday use the JPEG images remain fully functional, look like ordinary photographs, and do not attract attention. Even if the additional data is detected, it does not reveal whether it is an encrypted container, write artifacts, or simply an arbitrary sequence of bytes. Why JPEG First, JPEG is the most widely used image format. Photographs in this format naturally accumulate on computers and phones and are shared between people. Second, this format has one important property: a JPEG file ends with a special EOI marker. Everything written after it is ignored during image decoding, so the additional data does not affect how the image is displayed. This is the area we will use: JPEG JPEG Additional data EOI data │ │ │ ...░░░░░░░░░░░░░▓▒▒▒▒▒▒▒▒▒▒▒▒▒▒ At first glance, the solution looks extremely simple: just split the encrypted container into fragments and write one fragment to the end of each JP

Here is a question that sounds like a trick: can you build an accurate classifier out of models that are barely better than flipping a coin? Surprisingly, yes. That is the whole idea behind boosting, and AdaBoost is the algorithm that made it famous. I built it from scratch and dropped it into an interactive demo — here's how it actually works, real math, no hand-waving. Play with the live version: https://dev48v.infy.uk/ml/day21-adaboost.html The weak learner: a decision stump AdaBoost's building block is the simplest classifier you can imagine: a decision stump . It is a decision tree with exactly one split. Look at one feature, compare it to one threshold, and call everything on one side "+1" and everything on the other side "−1". That's it. One line, one cut. def stump_predict ( X , dim , thresh , polarity ): pred = np . ones ( len ( X )) if polarity == 1 : pred [ X [:, dim ] <= thresh ] = - 1 else : pred [ X [:, dim ] > thresh ] = - 1 return pred On anything that isn't trivially separable, a single stump is hopeless — on a checkerboard layout it barely passes 55-60%. That is exactly why it's a "weak learner": a model that only beats random guessing by a hair. The magic is in how we combine hundreds of them. Sample weights: a moving spotlight The engine of AdaBoost is a weight on every training point that says "how much does getting this one right matter?" Everything starts equal: n = len ( X ) w = np . full ( n , 1.0 / n ) # uniform: every point weighs 1/n These weights are a probability distribution — they sum to 1. After each round they change: points we got right get lighter, points we missed get heavier. Since we always pick the next stump to minimise weighted error, the heavy points end up dominating the search. The next stump is effectively forced to stare at whatever the committee keeps blowing. Weighted error, not a plain count When we hunt for the best stump each round, we don't count mistakes — we add up the weight of the mistakes: def weighted_error

For years I copied cron expressions off Stack Overflow, pasted them into a config file, crossed my fingers, and moved on. 0 9 * * 1-5 ? Sure, that "looks like weekday morning." */15 * * * * ? "Every 15 minutes, probably." I never actually read them. So I did the thing that always cures this for me: I built a tool that parses a cron expression, explains it in plain English, and shows the next five times it will fire. No library. About 50 lines of real logic. Here's everything I learned. The five fields (and the order that trips everyone up) A standard cron expression is exactly five fields separated by spaces: ┌──────── minute 0 - 59 │ ┌────── hour 0 - 23 │ │ ┌──── day - of - month 1 - 31 │ │ │ ┌── month 1 - 12 │ │ │ │ ┌ day - of - week 0 - 6 ( 0 = Sunday ) * * * * * The order never changes, and the number-one beginner mistake is swapping the first two. Minute comes first. If you write 9 30 * * * thinking "9:30am," you actually get "minute 9, hour 30" — which is invalid, because hours only go to 23. Say it out loud every time: minute, hour, day-of-month, month, day-of-week. Each field answers one question: which values of this unit does the job run on? An * means "every value." Most real schedules pin down a couple of fields and leave the rest as * . Daily at 9am is 0 9 * * * — minute and hour fixed, everything else "every." Lists, ranges, and steps Beyond single numbers, each field understands three operators, and they combine: Comma makes a list: 1,15 in the day field means the 1st and the 15th. Hyphen makes an inclusive range: 1-5 in the day-of-week field means Monday through Friday. Slash makes a step, taking every n-th value: */15 in the minute field means 0, 15, 30, 45 . Steps can apply to a range too, so 0-30/10 means 0, 10, 20, 30 . That's the whole grammar. Number, list, range, step. Once you can expand a field into the concrete set of numbers it matches, you understand cron. Here's the expansion function, which is the heart of the parser: function expandFie

Tree of Thoughts was a genuine leap. Instead of reasoning in one straight line, it branches into several lines, scores them, prunes the dead ends, and searches for the best path — so a puzzle that would sink a single chain of thought becomes solvable. But a tree has one restriction baked right into its shape, and once you see it you can't unsee it: every node has exactly one parent. A branch can be extended or abandoned. It can never be combined with another branch. That matters more than it sounds. Real problems decompose, and when they do, different branches each get part of the answer right. Branch A nails the first half; branch B nails the second half; neither is fully correct on its own. A tree is forced to pick one and throw the other's good half away. Graph of Thoughts (GoT) removes exactly that restriction. 🕸️ Interactive demo (a real merge-sort that branches, merges, and refines — with live-verified scores): https://dev48v.infy.uk/prompt/day21-graph-of-thoughts.html The core idea: thoughts are nodes in a graph GoT reframes reasoning as building a graph . Each vertex is a thought — a partial solution or intermediate result. Each edge is an operation that produced one thought from one or more others. Because it's a general graph and not a tree, a thought is allowed to have several parents, and edges can even loop back on themselves. That single change in the data structure is the entire conceptual leap. Everything else is just the operations you're now free to run on that network. The four operations generate (branch) — the familiar move, straight from Tree of Thoughts. From one thought, produce several different next thoughts. This can also be a split : break the input into independent sub-problems solved on separate branches. Diversity matters here — near-duplicates waste budget. score / rank — turn each thought into a number so the controller can compare them. Objective scorers win: a validator, a test, a metric. In the demo, the scorer is deliberately con

NBCUniversal executives are about to find out whether Peacock will sink or swim in the streaming industry. Now that Comcast is planning to split NBCUniversal, Peacock, and Sky from its broadband and wireless businesses, Peacock will be forced to stand on its own - without the backing of a combined company that pulled in more […]

A timely tale about a 50-year-old robotic arm...

The future of video game preservation just took a major hit. This morning, Sony announced that, starting in January 2028, the company will no longer produce physical PlayStation discs, which means that from that moment on you can only purchase new PS5 games digitally. At the same time, Sony also announced that it's going to […]

Dbrand apparently knows how to really whiff a product launch, as we recently saw with the Steam Machine Companion Cube shell. But it also makes the best case out there for the Nintendo Switch 2, which happens to be discounted right now. The Killswitch 2 is versatile, with better build quality than other similar models, […]

Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9. That won’t work every time—but if it…

Venice AI is already profitable, with annualized run-rate revenues of over $70 million, CEO Erik Voorhees said.

Google's 24/7 agentic assistant, Gemini Spark, comes to Mac alongside other improvements, like real-time tracking and support for more apps.

Sony will stop producing physical discs for all new PlayStation games beginning in 2028, as the company embraces an all-digital future.

Instacart redesigned its personalized marketing system using a configuration-driven multi-tenant architecture on Storefront Pro. The system replaces retailer-specific implementations with a shared execution engine, enabling scalable personalization, faster configuration propagation in under a minute, and 99.9% delivery success across hundreds of retail banners through a unified campaign platform. By Leela Kumili

Cassie Shum discusses the architectural evolution of GraphRAG and why data foundations are critical for advanced AI workflows. She explains how traditional vector RAG falls short when addressing global context, multi-hop reasoning, and provenance. She shares enterprise strategies for building semantically structured knowledge graphs that shift raw orchestrating logic down to the data layer. By Cassie Shum

The Builders Stage is returning to TechCrunch Disrupt 2026, bringing together 10,000+ founders, startup operators, and investors for practical conversations. and Q&A on what it takes to build and scale successful companies. Register now to save up to $330.

Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.

RIP to physical PlayStation discs.

Convenience costs you with Plex, but the alternatives require more work.

The acquisition was approved without concessions by the Department of Justice in June.

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