标签：#AI

The actor and investor is joining forces with Morgan Beller, who was previously a GP at NFX, to invest in early-stage startups.

The problem no one was solving Every Algerian developer building with AI hits the same wall: an international payment card. OpenAI, Anthropic, Google — every major AI provider assumes you have one. Most Algerian developers don't, or don't want to deal with the friction of currency conversion, card rejections, and unpredictable billing in a foreign currency. That's not a minor inconvenience. It's a barrier that quietly excludes an entire generation of developers from building with the best AI models available — not because they lack the skill, but because of infrastructure that was never designed with them in mind. The vision: AI sovereignty, not just AI access Access alone isn't the goal. The goal is sovereignty — Algeria having its own AI infrastructure layer, controlled locally, billed locally, and built to local compliance standards, instead of depending entirely on foreign gateways with no local accountability. That's what DEVUP AI is: Algeria's first AI inference gateway, built from the ground up to remove every friction point between an Algerian developer and the AI models they need. What DEVUP AI actually does 170+ AI models — including DeepSeek V4, Llama 3.1 405B, Qwen 3, Gemma 2, Mistral, GPT, Claude, and Gemini — through a single API OpenAI-compatible and Anthropic-compatible — point your existing SDK at our endpoint, no code rewrite needed Local DZD billing via Edahabia/CIB — no international card required SATIM-certified payment infrastructure — full compliance with Algeria's national payment standards Scoped JWT authentication for production-grade security A dedicated SDK ( npm install devupai ) and full documentation, so integration takes minutes, not days The technical bar was non-negotiable: this had to be production-grade from day one, not a side project. SATIM certification alone meant building proper transaction validation, receipt generation, chargeback tracking, and rejection-rate monitoring — the same rigor a bank would expect from a payment pr

CodeTrace-AI v1.0.1 — Stop Reading Code. Start Understanding It. Every developer has experienced this. You clone a repository, open it, and suddenly you're staring at thousands of files. You spend hours answering questions like: Where is this function called? Which files depend on this module? What happens if I modify this class? Is this code even used anymore? Traditional tools like grep , IDE search, or AI chat assistants can help you find code. They don't help you understand the architecture . That's why I built CodeTrace-AI . What is CodeTrace-AI? CodeTrace-AI is an AI-powered code intelligence tool that transforms your repository into a searchable structural knowledge graph. Instead of treating your project as plain text, it understands your codebase structurally by analyzing: 📂 Folder hierarchy 📄 Files 🏛 Classes ⚙ Functions 📦 Imports 🔗 Function calls 🌐 Cross-file dependencies Think of it as having an AI Software Architect that understands your entire repository. 🚀 What's New in v1.0.1 This release focuses on speed, privacy, and understanding large repositories. 🕸 Interactive Code Graph One of the biggest additions is the interactive repository graph. Instead of reading hundreds of files manually, you can visualize relationships between: Folders Files Classes Functions Imports Function calls Understanding a new project becomes dramatically easier. ⚡ SHA-256 Delta Sync Engine One feature I'm particularly proud of is the new Incremental Indexing Engine. Most code intelligence tools rebuild their entire index every time. CodeTrace-AI doesn't. It computes a SHA-256 fingerprint for every tracked file and detects: ✅ Modified files ➕ Newly added files ❌ Deleted files Only those files are: Re-parsed Re-embedded Re-added to the knowledge graph Everything else is skipped. This makes repeated indexing dramatically faster, especially for large repositories where only a few files change between runs. Under the hood The sync engine includes: SHA-256 fingerprinting Parallel f

I’ve always thought building a mobile app required climbing a massive learning curve just to get a basic environment set up. To test that theory, I tried building my very first Android app using Google AI Studio . Five minutes later, I had a working prototype. The coolest part about this isn't just the speed: it’s that anyone can do this. The traditional barriers to building software are disappearing, making it incredibly easy to just start creating. I recorded the whole 5-minute process here if you want to see what it looks like in practice: What's in the video Prompting AI Studio to build a native Android app from scratch Progressive Webapp (PWA) vs Android Native App in 2026: feature comparison Sideloading the app onto an Android device via USB-C cable. No Play Store required What happens when the AI gets something wrong? Fixing bugs in a vibe coded app

The AI neocloud provider, which specializes in hosting open source models, last raised at a $3.3 billion valuation in early 2025.

As of May 2026, more than 80% of the code Anthropic ships is written by Claude, not by its human engineers. The company disclosed the figure in an essay called When AI builds itself , with coverage from Tom's Hardware and VentureBeat . Key facts What: Anthropic says more than 80 percent of the code it ships is now written by its own model, Claude, and the more interesting numbers are about judgment. When: 2026-06-23 Primary source: read the source Two years ago this share sat in the low single digits. The shift accelerated after Anthropic released Claude Code , a tool that lets the model read an entire codebase, make changes, run tests, and fix what breaks without human help. The human role has flipped: engineers used to author the code while the machine assisted; now the machine authors the code and engineers review, approve, reject, and steer. Anthropic reports its typical engineer ships roughly eight times as much code per quarter as a few years ago — not because people type faster, but because they spend their day reviewing the model's output instead of writing from scratch. Think of it as a newsroom where a tireless junior writer drafts every article and senior editors only sign off. Volume goes way up. But the 80% figure is less impressive than it sounds: a draft that a human must check, fix, and approve is not the same as a writer you can leave unsupervised. Most of those lines still pass through a person. On its own, this number measures effort the machine saves, not work it can be trusted to do without oversight. The results buried deeper in the essay matter more, because they concern taste rather than volume. Anthropic ran a recurring test where the model chooses the best next step in a research project, then compared its choices against its own scientists. Late last year the model was roughly a coin flip against the humans. By spring 2026, an unreleased internal model was picking the better direction clearly more often than its own researchers. Choosing w

Are you worried your AI chatbot is trying to build a bomb or leak personal information about you? There’s a website for that.

Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.

After a lengthy legal dispute, Krafton has settled with its subsidiary Unknown Worlds Entertainment, which is developing Subnautica 2, and will pay bonuses to the studio's staff, Bloomberg reports. The dispute began last year after Krafton pushed out Unknown Worlds' cofounders, Charlie Cleveland and Max McGuire, and its CEO, Ted Gill, ahead of a potential […]

"You gods don't speak in ways we understand."

US lifts curbs on Anthropic’s advanced Fable and Mythos models.

These six free settings will not make your project unhackable. Nothing will. What they will do is close the easy doors. Turn these on, and your project will be meaningfully harder to attack than it was before. The post 6 security settings every GitHub maintainer should enable this week appeared first on The GitHub Blog .

A ChatGPT subscription starts at $20 a month and is one of the cheapest ways to run inference. OpenAI has also been fairly relaxed lately about third-party agents using them , which makes the deal even better for a lot of us. But a subscription can't be used as freely as pay-per-token access , and the providers police the difference. Anthropic recently narrowed its subscriptions to first-party apps; OpenAI has its own limits. Here's what will get you banned from an OpenAI subscription. Sharing your subscription A ChatGPT subscription is strictly personal. One subscription, one user. Sharing yours breaks OpenAI's terms of service. That also covers account pooling and account rotation, where several people share the same credentials to dodge rate limits. Running it in automation Automation (CI, runners, schedulers) should run on per-token pricing, not a subscription . Once a system calls the OpenAI API with your token while you're not in the loop, the usage stops being personal. No unattended production system should run on a ChatGPT subscription. Serving other users For now, you can point an autonomous agent like OpenClaw or Hermes at your ChatGPT subscription, as long as it only talks to you. The moment that agent starts chatting with other people, or serving them in any way, it turns into a team use case , and that inference should be paid per usage. Putting it in a commercial product Same logic here. Making an LLM call authenticated with an individual ChatGPT subscription inside a product you ship breaks OpenAI's terms. That access is subsidized, and reselling it in any form isn't what it's meant for. If you've built something just for yourself and you're the only user, you're probably fine. The bottom line A ChatGPT subscription is personal . Anything that stretches past personal use can get you restricted or banned. If you're not sure your usage counts, move it to pay-as-you-go. If you want to keep the subscription for your own work and fall back to per-token pr

Hey DevHunt community! 👋 I'm incredibly excited to launch Scankii! As developers, we are building more and more AI Agents using frameworks like LangChain, OpenHands, and AutoGen. The standard paradigm is giving these agents "skills" or "tools" — which are basically just Python functions combined with Natural Language instructions (prompts or docstrings). But here is the problem: Standard secret scanners (like GitLeaks or TruffleHog) are blind to AI-specific vulnerabilities. They only scan source code for hardcoded secrets. But what if your Python code securely loads an API key, and your English instructions accidentally trick the agent into printing that key to stdout? The agent framework captures that output, injects it into the LLM context window, and your secret is suddenly exposed. We call this Cross-Modal Leakage. Enter Scankii. 🛡️ Scankii solves this by analyzing the intersection of your Natural Language and your code. It uses a dual-engine pipeline (NL Semantic Analyzer + AST Syntax Analyzer) to track variable flows between your prompts and your code sinks. ✨ Core Features: Dual-Engine Scanning: Correlates English instructions with Python ASTs. Local-First & Fast: Your proprietary agent tools and code never leave your machine. CI/CD Ready: Outputs standard SARIF reports. Drop it into GitHub Actions or use it as a pre-commit hook. Framework Agnostic: Works with LangChain, AutoGen, CrewAI, MCP, or any custom python agent framework. I built Scankii to give developers peace of mind when scaling their agent toolchains. Security shouldn't be an afterthought when building autonomous systems. I would love for you to try it out on your agent repos, star the project, and leave any feedback or questions below! I'll be here all day answering them. 👇 GitHub Repository: https://github.com/ashp15205/scankii Installation: pip install scankii

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

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 […]

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 […]

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.