Still no price in sight.

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...

I currently have a website which heavily relies on the Google Places Photo API. Users can scroll through a bunch of POIs for planning trips, as well as various other things. This can mean that each user could 50+ unique photos. I understand that it’s against ToS to cache these photos and reuse them. The API costs are too high to maintain a scalable solution, but no real competitors come close to the quality of Google. At this current rate I’ll probably have to cap my users on the amount the amount of requests they can make. Is there some work around that I’m missing? Thanks submitted by /u/TWJ32 [link] [留言]

To my knowledge, this is the first formally verified implementation of an intersection algorithm for polygons. The experience of working with AI agents on this project changed a lot with recent model releases, as I describe in the readme. Opus 4.8 is able to provide algorithm implementation with formal proof in one shot, whereas previous models required me to provide proof strategies in multiple steps. Trust in the correctness comes entirely from the Lean checker and human review of a small spec

If you have never written a line of game code and you are trying to choose between Unity, Godot, and Unreal, the internet will give you fifty contradictory answers in your first hour of searching. This article is the answer we give to people who ask us in person. We are a Unity-specialist studio. I have spent 12 years building games, including a tenure as Mobile Team on RuneScape Mobile at Jagex. Over that time I have mentored a steady stream of new developers entering the industry. We picked Unity for our commercial work, deliberately, and we will say upfront where that lens does and does not serve you. The advice below is what I would tell a friend's teenager who asked which engine to learn first, not the version where I am trying to win you as a client. Three things drive whether a beginner finishes their first game or quietly abandons it: the engine's first-week friction, the quality of the free learning material, and whether the language and tools punish you or reward you when you make a mistake. Comparison articles obsess over feature lists. Beginners obsess over whether they can get something on screen by Saturday. We are going to talk about Saturday. The 30-Second Answer If you skim nothing else, take this: Pick Godot if you want the gentlest first week. The editor is small, the language reads like Python, and you can ship a 2D game to the web in a single afternoon. Best chance of you actually finishing a project. Pick Unity if you want a future career in the games industry. Largest tutorial library, biggest job market, most transferable skills. C# is harder than GDScript but every hour you spend on it pays back in the long run. Pick Unreal if you have always wanted to make games specifically because of the visuals. Blueprints let you avoid C++ at the start, the rendering looks beautiful from day one, and the long learning curve has the highest payoff if you commit. For the rest of the article, we will explain why each of those is true, where the engines gen

I Read Your AI Agent Logs So You Don't Have To: A $149 Service That Beats Another Dashboard What if the cheapest fix for your broken AI agent is a stranger reading 40 hours of traces for $149? I spent the last month doing exactly that — reading roughly 40 hours of production logs from teams running LangGraph, CrewAI, and AutoGen agents for paying customers. Not building observability dashboards. Not comparing LangSmith vs Langfuse. Reading the actual traces and writing up what was wrong, what to fix, and in what order. Three observations from those 40 hours: The dashboard was never the problem. Every team already had LangSmith or Helicone or a homegrown equivalent logging every LLM call. None of them were reading the logs. The "fix" was almost always one of seven patterns. I kept seeing the same shapes — stuck retry loops, idempotency gaps, tool-call argument drift, etc. — dressed up in different framework jargon. The teams that asked for "another tool" were the ones least likely to use it. They had 14 tools. The teams that paid for an hour of my time were the ones who said "I don't have time to look at this myself." That second group is who I'm now building a $149 service for. Here's why I think it works, what the deliverable looks like, and where the limits are. Why a $149 fixed-fee reading and not an hourly rate I tested three pricing models against the same deliverable: a written diagnostic of an agent's last 7 days of traces, prioritized fixes with code-level examples, and a 30-minute async follow-up. Model Conversion Avg revenue / inquiry Notes $200/hr (estimated 3hr) 2/40 inquiries $15 (lost 38 to sticker shock) Freelance default, fails on cold traffic $1,500 flat project 0/40 inquiries $0 Above the "I'll just keep it broken" threshold for most small teams $149 fixed diagnostic 11/40 inquiries $41 Below "another contractor" threshold, above "free advice" The $149 number is the inversion point — low enough that a stressed eng lead can expense it without a meet