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Open source agentic development workspace Discussion | Link

Most of the validation work on vaas-x so far had been industrial sensor data — turbofans, machine telemetry. I wanted to know if the same zero-config channel classifier actually transfers to a completely different domain: a wearable IMU strapped to a moving human. No feature engineering, no per-sport tuning, no hints about what any channel means. I'm writing this one up slightly differently than my other posts, because the first version of this test gave me a wrong answer, and I think the reason it was wrong is more useful than the result itself. The dataset UCI's Daily and Sports Activities set (Altun, Barshan & Tunçel, 2010): 8 subjects, each wearing five Xsens IMU units — torso, both arms, both legs — 9 axes per unit (accelerometer, gyroscope, magnetometer × x/y/z), sampled at 25Hz. 45 channels total. It includes both a sedentary activity (sitting) and dynamic sport activities (basketball, rowing), which gives a clean, checkable question: does a classifier that's never seen this data correctly tell apart "person sitting still" from "person playing basketball," using channel statistics alone? import pandas as pd # Mirrored subset: github.com/AniMadurkar/Daily-Activities-and-Sports-Biomechanics-Analysis df = pd . read_csv ( " sports_science_dataset_subset.csv " ) channels = [ c for c in df . columns if c not in ( " subject " , " activity " , " timestamp " )] print ( len ( channels ), " channels " ) # 45 First attempt — and the mistake My first pass pooled all 8 subjects together per activity and ran it through the profiler in one shot. The result came back backwards: sitting showed up with more "significant" channels than basketball. That's not just unexpected, it's physically nonsensical — a person sitting still should be one of the lowest-variance activities in the entire dataset. The bug wasn't in the classifier. It was in the test. Pooling subjects together means each subject's own sensor baseline and IMU orientation differences get mixed into the between-subje

BMW is forcing a weird Spider-Man ad onto its dashboard displays

Memory and storage prices will be inflated for the foreseeable future, so we’re always happy to find a good deal on capable gaming hardware, like this discount on the Lenovo Legion Go S with a Z2 Go processor and SteamOS. Normally $1,137.41 (and selling for close to $1,000 at Best Buy), Lenovo has the handheld […]

Waymo has dropped the waitlist for its robotaxi service in Dallas, the latest step in the company's bid to scale its self-driving technology across the United States, U.K., and Europe.

Most Windows 11 users don't need to bother with the Pro version of Microsoft's operating system.

Starting today, you can take an additional $100 off your founder, investor, or attendee TechCrunch Disrupt 2026 pass, which is a nice bonus on top of our current discounted pricing.

When a premium car brand like BMW says it has a "special surprise" in store for drivers, I'd expect something more luxurious than having a movie commercial beamed onto the dashboard. That's exactly what's happening to many BMW owners, however, who are being shown banner ads for Spider-man: Brand New Day on their Control Display […]

Bingers is a new TV and movie tracker that revives the social features fans loved with TV Time, while adding support for importing their viewing history.

Instead of one huge, un-reviewable pull request, teach coding agents to decompose work into a clean, ordered stack with GitHub stacked pull requests. The post Turn one giant AI-generated pull request to a reviewable stack appeared first on The GitHub Blog .

If you're done giving Sony money every month, cancelling PlayStation Plus is easy.

A security vulnerability in the cryptocurrency hardware wallet Coldcard is allowing hackers to drain the crypto from victims’ wallets. The total losses amount to more than $130 million, according to blockchain-monitoring firms.

Quotient CEO Lizzie Matusov explains why soaring AI spend often fails to improve software delivery. She presents a research-backed AI maturity framework designed to help engineering leaders move beyond vanity metrics like token usage, align organizational AI adoption, and address critical bottlenecks across the software development life cycle to deliver measurable business outcomes. By Lizzie Matusov

Apple is requesting expedited discovery in an ongoing lawsuit against OpenAI.

Congress set ownership limit at 39%, but FCC claims authority to kill the rule.

Spotify says Merlin, which represents more than 30,000 independent labels and distributors, has joined Universal Music Group in backing its upcoming AI-powered remix and covers product. The paid tool will let fans create AI-generated covers and remixes of participating artists’ music while ensuring artists opt in, receive credit, and are compensated.

Almost every tool-governance layer I have looked at writes its log after the call returns. Some write it in a finally . Some batch it. Some hand it to a logging framework that flushes on its own schedule. That ordering quietly decides what your log can be used for. If the record is written after the body runs, then a record that is missing has two possible explanations, and nothing in the file distinguishes them: The call was never authorised, so it never ran. The call was authorised, ran, did its work, and the process died before the log line reached disk. Those are not close together. One is the control working. The other is an unlogged deletion. When someone asks you six weeks later what your agent was permitted to do at 03:14, "there is no line for it" answers nothing. So I wrote a small library that inverts the order. obstat obstat is an auditable decision record for agent tool calls. Nihil obstat — nothing stands in the way — was the formal clearance a censor granted in writing, before publication . That is the whole idea. from obstat import guard @guard ( resource = " doc:{doc_id} " ) def delete_document ( doc_id : str ) -> str : ... An agent asks to do something, a rule decides, and the decision goes to disk — written and fsync ed — before the tool body executes. If the process dies mid-call, the record still says what was authorised, for whom, against which resource, and why. record.decision() returns only after the fsync returns. Not flushed after, not deferred, not batched. Everything else in the library is convenience; this is the part an examiner relies on. The claim has a test, not a paragraph An architectural promise nobody can falsify is marketing. This one is checked by reading the log from inside the tool body — the one place where anything buffered, deferred, or written afterwards is invisible: def test_record_is_durable_before_the_body_runs ( workspace ): workspace ( ALLOW_ALL ) seen : dict [ str , list ] = {} @guard () def read_thing ( what : st

Canonical: this is a cross-post. The original lives at https://four-leaf.ai/blog/openai-interview-process Most OpenAI interview prep hands you a list of hard coding problems and tells you to grind. That calms the nerves and misreads the loop, because at OpenAI the coding bar sits next to something the grind can't touch: a genuine point of view on where AI is going and how it could go wrong. Candidate-facing guides describe that thread running from the first recruiter call to the final behavioral round. You can solve every problem and still stall if you can't hold that conversation. We've mapped the loops at Amazon , Google , Apple , Meta , and Bloomberg by reading each process through how the company actually runs. The map now includes the other AI labs and high-growth names candidates weigh alongside it, including Anthropic , SpaceX , and Robinhood . OpenAI is the one candidates most often prepare for as if it were a standard FAANG gauntlet. It isn't. The coding is practical rather than puzzle-flavored, a whole round asks you to present and defend work you built, and the loop varies more team to team than almost any large employer. Generic big-tech prep leaves you exposed on exactly the parts specific to OpenAI. A note on sourcing. OpenAI doesn't publish its interview process. There's no stage list, no scoring rubric, no candidate-facing equivalent of Google's structured-interviewing guidance. So this map comes from reputable secondary sources that collect named and dated candidate accounts, primarily interviewing.io's OpenAI question guide and Exponent's OpenAI software engineer guide . Where those accounts agree, this guide states the pattern. Where the loop varies or the record thins out, it says so rather than inventing detail. Treat everything below as the common shape, not a guaranteed sequence. Why the loop varies so much Start with the thing that makes OpenAI different to prep for. Hiring is decentralized, and secondary guides are blunt that the loop varies

Texas announced new a audit on data centers that could slow approval for new facilities seeking to connect to the state energy grid. Governor Greg Abbott (R) on Monday directed the Public Utility Commission of Texas (PUCT) and the Electric Reliability Council of Texas (ERCOT) to verify and audit new data center proposals, writing that […]