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Manna is launching a U.S. operations and manufacturing facility in Tulsa, Oklahoma, that will eventually employ 1,000 people.

The FTC sued the famed green tractor company last year.

Even though they haven’t been officially announced yet, Samsung is giving you a chance to save some cash when you preorder what we’re expecting to be the brand’s updated Galaxy Z Fold phones. The next Galaxy Unpacked event will take place on July 22nd, 2026, and features the tagline “A new shape unfolds.” In addition […]

More young girls sue X over Grok CSAM; X accused of shielding child predators.

Matthew Danzeisen’s lawyer says the case is a “shakedown about a bag” that brushed someone’s leg. Stefanie Bojar says she was injured aboard the jet—and that the lawsuit is a bullying tactic.

In June, we experienced six incidents that resulted in degraded performance across GitHub services. The post GitHub availability report: June 2026 appeared first on The GitHub Blog .

Elon Musk's tech company released the newest version of Grok on Wednesday, promising a cheaper, more efficient alternative to other powerful AI models.

This developer didn't expect his side project to grow to 300,000 users, but people love Roost because it's an alternative to an always-on, fast-paced online culture.

General Intuition is betting millions of hours of video game data can train the foundation models for physical AI, making it easier to build smarter robots with minimal real-world data.

PC shipments are falling, but revenue for manufacturers is still rising.

Both vulnerabilities allow untrusted users to gain root privileges.

AI coding agents are becoming good enough to touch real codebases. They can refactor files, write tests, change architecture, move logic around, and sometimes modify more code in ten minutes than a human would in an afternoon. That is powerful. But it creates a new debugging problem. Git can tell you what changed . When an AI agent was involved, you often need to know something deeper: Why did this change happen? Which prompt caused this line? Which model produced it? What files did the agent read before writing it? What later changes depended on this agent action? That is the problem I wanted to solve with Causari . Causari is a local CLI for intent-addressable code . It records AI agent actions as causal events: prompts, models, reads, writes, diffs, reasoning, cost, and relationships between actions. The goal is simple: Git tracks bytes. Causari tracks intent and causality. Repository: https://github.com/croviatrust/causari Website: https://causari.dev The problem When a human developer changes code, there is usually some context. A commit message. A pull request. A ticket. A discussion. A design decision. With AI coding agents, the workflow is different. You ask something like this: Refactor the auth flow and add JWT refresh logic. The agent reads files, makes assumptions, writes code, maybe fixes tests, maybe changes something unrelated, then moves on. At the end, you have a diff. But the diff does not tell the full story. A suspicious line appears in auth.ts . Git can show when the line appeared. But Git cannot answer: which prompt produced this exact line? what completion did it come from? did the agent read the right files first? was this part of the original request or an accidental side effect? if I revert this, what downstream work am I also undoing? That gap becomes bigger as agents become more autonomous. The more work agents do, the more we need provenance. Not only code provenance. Intent provenance. The idea: intent-addressable code Causari treats an

Open-source workspace for AI agents and workflows Discussion | Link

As part of Microsoft's big Xbox "reset," which includes layoffs affecting 3,200 staffers, jettisoning studios, and shifting investments to focus on "higher priority projects," Obsidian Entertainment is changing its plans. The studio, behind games like Grounded and The Outer Worlds, is starting work on a new Fallout title and has canceled "multiple projects," including a […]

I'm building TrainWiz , a Flutter app that turns real exercise into a pet-raising game: you do squats or push-ups, your phone counts the reps, and a little creature levels up and evolves. The core technical problem sounds trivial and absolutely is not: count reps from the camera, on-device, without uploading a single frame. Here's what broke along the way, and what finally worked. Why on-device Two reasons: privacy and latency. A fitness camera that streams your body to a server is a non-starter for most people, and rep feedback has to feel instant or the whole "game" loop dies. So everything runs locally with tflite_flutter + an on-device pose model — no footage ever leaves the phone. Naive attempt #1: joint-angle thresholds The obvious approach: track the knee angle, count a rep when it dips below X° and comes back up. // looks fine in a demo, dies in the real world final kneeAngle = angleBetween ( hip , knee , ankle ); if ( ! _down && kneeAngle < 100 ) _down = true ; if ( _down && kneeAngle > 160 ) { reps ++ ; _down = false ; } It demos beautifully. Then real users prop the phone on the floor, stand at an angle, and it falls apart. The trap: a phone camera gives you 2D pose. A "120° knee angle" flattens completely depending on where the camera sits — the same squat reads as 90° or 150° purely from perspective. Lifting to 3D via the model's z doesn't save you either; monocular z is noisy enough that the angle jitters across your threshold and double-counts. Naive attempt #2: a "body-line" gate Next idea: figure out which exercise you're doing so I can pick the right signal. Standing (squat) vs. horizontal (push-up) should be easy — just check if shoulder, hip and heel form a straight line, right? Wrong, again for the 2D reason. In a real push-up shot from the front-corner, shoulder–hip–heel are not collinear on the image plane — perspective bends them. I gated push-up counting on "body is a straight line" and it would just... stop counting mid-set. Nothing is more

X plans to send users direct messages when posts they’ve liked, replied to, or reposted receive Community Notes, an update aimed at addressing criticism that the platform’s crowdsourced fact-checking system often arrives too late to curb misinformation.

The horror-comedy series Widow's Bay earned 19 Emmy nominations for its first season.

LLM agents burn a ridiculous number of tokens on redundancy: opening the same files again and again, trying a patch, failing, then wandering back through the repo like they’ve never seen it before. A July 2026 paper, ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair , puts real numbers behind that waste. In repository-level repair, agents keep dragging in irrelevant code and logs. ContextSniper tackles that with a context layer built around tiered memory and an intention-aware context gate that filters low-value regions before they ever reach the model. That gate alone cut tokens by 51.5% on one host agent and 38.9% on Claude Code, while submitted-resolution rates stayed basically in the same neighborhood. The gate is the interesting part, because it is not tied to that paper’s exact system. It is a more general idea, and it is starting to show up across agent architectures. At heart, the gate is just a classifier. Given a request, it has to decide what kind of retrieval will answer the question cheapest: symbol lookup, semantic search, graph impact, mutation prep, or something else. That leads to the practical question the paper does not really answer: Do you need another LLM call just to decide what context to retrieve? We tested that directly. Five ways agents get code into context Before you can gate anything, you need a retrieval strategy. Most current systems fall into one of five rough families: Grounded read-only retrieval: parse the code and return exact symbol source by name. Byte-precise, no synthesis. Graph code intelligence: model calls, imports, entities, and dependencies as a graph, then traverse it. Embedding / RAG search: use vector similarity over chunks. Whole-repo packers: compress or dump the repo into the context window. Mutate / execute runtimes: retrieve context, then modify or run code. None of these is magic. Graphs are great for relationships, but they can drift away from source. RAG is useful, but f

The feature can do things like apply cinematic relighting to brighten up a dark clip, swap out a plain background for something fun, or add artistic styles to videos.

The company's streaming devices aren't quite keeping up with competitors in several important categories.