peektea past the roadmap 👀 sorting and scrollable previews
Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...
Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...
VSCode extensions were how Github were breached earlier this year. What are people doing around VSCode security best practices around extensions. Approved Extensions Only Disable Auto update Is there anything else like minimum age or settings like that can be done? submitted by /u/ruddet [link] [留言]
Valve now says that the delayed Steam Machine PC and Steam Frame VR headset are set to launch sometime this summer. In a Thursday blog post detailing its Verified programs for both pieces of hardware, Valve concludes by saying that "We're excited for players to try your titles on the new Steam hardware once they […]
The prediction that equivariance reduces sample complexity by a factor of |G| appears in roughly every paper on geometric deep learning and is measured as an actual scaling law in roughly none of them. This paper does the measurement. The methodology is the interesting part. Naive estimators conflate group order with task difficulty (larger groups induce harder symmetry structure, not just more constraint), so the authors derive a relative exchange rate that cancels the shared difficulty out, meaning roughly how much less data the equivariant model needs compared to a vanilla baseline as a function of n, on a controlled C_n-symmetric task where n is a free knob. They also pre-specify a failure taxonomy: explicit conditions that would count as evidence against the hypothesis before seeing results. The headline number is beta_diff ~ 1.28, consistent with the theoretical 1.0. But the more durable finding is the wrong-group control : a model built with the wrong cyclic symmetry, same orbit size and same compute budget, is actively worse than no constraint. Not noise. The joint pairwise CI [+0.79, +3.26] excludes zero robustly across every estimator they run. Misalignment isn't just unhelpful; it is harmful. There is also a clean mathematical result slipped into Sec. 4.3: augmentation + test-time orbit averaging is exactly equivariant for output-pooling architectures, provably and verified to bit-identical training curves. The architecture-vs-augmentation gap collapses to whether you apply the orbit average at test time, not to anything structural. This seems underappreciated. The paper is unusually transparent about what it didn't nail: the relative-rate estimator was adopted post-hoc, the two-level bootstrap CI (seeds x group sizes) includes zero, and a finer-N replication on a sqrt(2)-spaced grid is inconclusive. They rank their findings explicitly by robustness. The wrong-group result is the one they would stake a claim on. The exchange rate is directionally probable
Anthropic has been growing at a breakneck pace. The company announced that annualized revenue crossed $47 billion in May, up dramatically from roughly $9 billion at the end of 2025. That trajectory faces a real test, though.
The Airbnb CEO said last year it hasn't struck an LLM partnership because existing products weren't quite ready.
Now that almost everyone can use AI tools, I’m curious what skill will actually separate people moving forward. Is it prompting? Taste and judgment? Knowing how to verify outputs? Domain expertise? Workflow design? Or something else? My current take is that AI makes execution faster, but it does not replace knowing what good work should look like. The people who can guide, check, and apply AI well may become more valuable than people who only know how to generate outputs. What skill do you think will matter most in the next few years? submitted by /u/GlobalOpsNotes [link] [留言]
Yeah, so "AI will eat the world" or "AI changes everything" - well, its certainly changed traffic patterns on the web. submitted by /u/Objective_Farm_1886 [link] [留言]
This is a super dumb question (considering I've been working for like 5-6 years in this field) but I just want to get to the bottom of this so I can finally stop worrying that by searching "frontend developer" isn't reducing my future prospects LOL. submitted by /u/WitnessConfident2451 [link] [留言]
Robot demonstrations can distort public perceptions of robotic capabilities.
A few weeks back I had an agent reconciling a vendor list. It ran clean. No error, no crash, output...
India's largest food delivery platform processes over 1.5 million orders every single day. One missed...