The more things change, the more they stay the same.

Submitted a short theoretical paper to TMLR and got desk-rejected with "does not meet our editorial standards or allow us to assess claims and evidence" and "not a suitable venue for this work." Is this a common outcome for first submissions? Curious what typically drives this kind of rejection, scope mismatch, insufficient experiments, or something else. Not looking to appeal, just trying to understand the bar so I don't waste time on the wrong venue next time. Anyone else gotten this and figured out what the actual issue was? submitted by /u/observer678 [link] [留言]

Apple's new Siri AI is finally here, and so far, it seems like it works. I have access and have been messing around with it, and my biggest impression so far is that Siri AI is quite curt - which I mean as a compliment. Many AI chatbots are cheery and wordy. While a more […]

Surprised there's no real tooling for this given how much research exists on continual learning. Built pyrecall to fill the gap. Snapshots skill scores before/after fine-tuning, flags regressions, rolls back LoRA adapters by name. Fully local, no external APIs. v0.1.0, MIT, pip install pyrecall Curious if anyone has thoughts on the benchmark design that's the part I'm least confident about. https://github.com/Arths17/Pyrecall submitted by /u/Level_Frosting_7950 [link] [留言]

A former xAI engineer is suing the company and SpaceX, alleging he was fired for raising AI safety concerns about Grok days before SpaceX's historic IPO.

The regulation also imposes new safety expectations on 'AI chatbot services.'

Andrew Yang’s 2020 presidential campaign was based on a warning that automation and AI would hollow out the labor market and concentrate wealth in the hands of a few. At the time, ideas like Universal Basic Income felt fringe. Now Dario Amodei, Sam Altman, and Bernie Sanders are all saying versions of the same thing. An entrepreneur at heart, […]

My frustration with distributing java apps didnt show up recently. I remember having implemented my first network jar downloaded back in the 2000's because i needed applet like feature support with desktop full control. Years after, the problem is the very same. Webstart didnt really took off and the only mean i had in my projects was the ugly fatjars, including the (for me) uglier spring-boot repackaging that changes the application classloading behaviour and hence giving me by time some headac

For days after the stunning incident, the ADA had doubled-down on the choice.

Been building EventInbox for a while — webhook delivery infrastructure for developers. Finally got the landing page live today: https://eventinbox.pro It's the layer that sits between your app and your customers' endpoints — durable delivery, automatic retries, HMAC-signed payloads, full observability, and replay. SDKs for TypeScript, Python, and Go. Still early but the API is live and signup works end to end. Would love honest feedback on the page and the product direction. submitted by /u/eventinbox [link] [留言]

Typing a few letters and numbers into my web browser, I find myself gaping at the identity documents of complete strangers. The passport of a young woman from Germany. The passport of a man from Spain with glasses resting on his head. The front and back of another man's driver's license, a stereotypically goofy expression […]

Asked it: "what did I waste on takeaways last month?" Answer: "You spent £340 on takeaways in May — 22% more than April. Want a £200 cap with a nudge near the limit?" £340. In one month. The "22% more than April" detail was the real knife. This is from my own app (Expenzez — it reads uploaded statements on-device, no bank login), so yes, I built my own roaster. But the broader point stands: AI answering questions from YOUR actual numbers beats generic budgeting advice by a mile. Best/worst thing an AI has told you about your own data? submitted by /u/biszaal [link] [留言]

Every hand-tracking demo shows you 21 dots. The interesting part is what nobody shows: turning dots into numbers someone can act on. Dots are a capability, not a product Run any modern hand-tracking model and you get 21 beautifully stable landmarks per hand at 30 FPS. Impressive — and by itself, worthless. No client has ever paid for dots. They pay for measurements : is this clearance compliant, is this part aligned, did this patient's range of motion improve. I learned this on utility infrastructure work, where the deliverable was never "we detected the wire" — it was the attachment height of that wire, and whether it violates clearance rules . Keypoints were step one of three. The demo: live metrics, not just a skeleton My portfolio's keypoint demo derives three measurements per hand, every frame: const wrist = lm [ 0 ]; const palm = distance ( wrist , lm [ 9 ]); // scale reference const pinch = distance ( lm [ 4 ], lm [ 8 ]) / palm ; // thumb tip ↔ index tip The crucial line is the scale reference . Pixel distances are meaningless — they change as you move toward the camera. Dividing by palm length (wrist to middle knuckle) gives a relative measurement that's stable under distance, and multiplying by the average adult palm length (~8.5 cm) converts it into an approximate real-world gap — the demo shows "≈ 3.2 cm" floating on the pinch line. In infrastructure work the same role is played by a known object dimension — a standard crossarm, a pole class height. Every measurement-from-pixels system needs its ruler. Finger counting is a geometric test (is each fingertip farther from the wrist than its middle joint?), and "hand openness" averages fingertip extension — three lines of geometry each, but they convert a model output into a readout a human understands instantly. Honest layering The landmarks come from MediaPipe's pretrained pipeline (palm detector → landmark regressor → gesture classifier, float16, WASM + GPU delegate) — Google's models, credited on the page