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Jan AI

OpenAl Announced vs. Current Operational Compute

submitted by /u/Business_Garden_7771 [link] [comments]

OpenAI cofounder Andrej karpathy just joined anthropic and the talent war is officially over

this happened literally today ,andrej karpathy one of the most respected ai researchers alive nd the guy whose youtube lectures taught half the developers in this sub how neural networks work, just announced he is joining anthropic's pre training team. He's the 3rd senior openai figure to defect to anthropic in under two years. Jan leike left in may 2024, John schulman (co-founder) left in august 2024 and now karpathy. He is joining the pre training team under nick josef and building a new team focused on using claude to accelerate pre training research which means Anthropic is betting that claude can help make itself smarter, thats recursive self improvement with one of the most capable researchers in the world leading it. The musk trial verdict came in yesterday with the jury ruling in altman's favor, karpathy announces today voilaa . The timing is either coincidental or the most savage talent acquisition move in tech history. I hv been watching this trajectory while building my own workflows on claude ,every month the ecosystem around claude gets stronger. The connectors mean claude orchestrates professional creative tools natively, the api means platforms like magic hour and kling can plug video generation capabilities into claude powered pipelines, the finance templates mean entire industry workflows run through claude and now the guy who built tesla's self driving stack is making the pre training better. Polymarket gives anthropic 67.5% chance of going public before openai and i too think its ipo will be more successfull than openai what's everyone's read on what karpathy specifically brings to claude's pre training? submitted by /u/Healthy-Challenge911 [link] [comments]

I Verified Every Anthropic Usage Promotion Since Aug 2025. Here's the Complete Timeline from Official Sources.

submitted by /u/Severe-Newspaper-497 [link] [comments]

Anthropic was supposed to be different. They're not anymore.l.

Paying Max subscriber here, building agent orchestration on top of claude -p and the Agent SDK. So this week's announcement directly hits what I'm working on. Over the last few months, Anthropic has moved like this: Jan 9: server-side block against OAuth tokens used outside Claude.ai and the Claude Code CLI. OpenClaw, OpenCode, Goose, Roo Code - all broken instantly. No real announcement, just an error message. Feb 19: legal docs quietly updated. Agent SDK now needs an API key. A new phrase appears: "ordinary, individual usage." Anthropic staff jump on X to say "nothing is changing." Docs say what they say. April 4: full ban on third-party agents using subscription credentials. Fair point on their side - some people were running 24/7 bots on a $200 plan burning thousands in tokens. But the rollout was rough and the comms were rougher. April 21: someone notices Claude Code is gone from the Pro plan on the pricing page. Support docs changed too. After the backlash, Anthropic calls it a "2% test of new prosumer signups." Reverted in 24 hours, but the trial balloon got popped. May 13: reversal. claude -p and the Agent SDK come back, but now under a separate credit pool that matches your plan price 1:1 - $20 / $100 / $200. Non-rollover. Billed at API rates. Effective June 15. If you were running real automation on Max, your effective inference value just dropped on the order of 25-40x by what the community is calculating. In the background: spring outages and quota tightening, and last fall's privacy pivot where consumer chat training defaulted on. Opt-out exists, but retention went from 30 days to 5 years for anyone who didn't opt out. Here's what's been bothering me. A lot of us paid Anthropic specifically because of the positioning. The lab that does things differently - safety-first, transparency-first, the responsible alternative to whoever else you thought was extracting from users at every turn. I knew part of it was marketing. The operational behavior backed it up, though. For a while. What's happening now is the playbook of every other AI company. Quiet doc edits. Three policy flips in two months. A 25-40x devaluation framed as a "simplification" and a "perk." Staff on X publicly contradicting their own docs in the same week. The vocabulary has shifted from "here's what we're building" to "here's what we're clarifying" - and that shift is the tell. Could be capacity panic from a company that grew faster than its infrastructure. Could be something quieter - if model improvements get harder to differentiate, business growth has to come from somewhere, and "somewhere" usually means tightening on the customers you already have. I don't know which one it is. What I do know is that the lab that sold itself as the alternative is now running the same playbook. Anyone else reading it this way? submitted by /u/rmmadl [link] [comments]

Claude Code vs Codex: 36 files vs 28, $2.50 vs $2.04, and one infinite loop. My full breakdown.

I've been using Claude Code for months. It's been solid. But with Opus 4.7 and GPT-5.5 both dropping in April, I wanted to see how Codex actually compares on real problems, not benchmarks. https://preview.redd.it/fkwjy5eg3y0h1.png?width=1540&format=png&auto=webp&s=e1df6e53f1164a6da0deabaafe53118cb01b171e Been meaning to do this for a while. Sick of seeing benchmark screenshots, so I just built stuff. So I built two tasks. Same prompts. Same MCP setup (GitHub + Slack). Same machine. Task 1: PR triage bot Read open PRs, score by complexity (files ×2, lines/10, +3 for no labels, +5 for no reviewers), write a markdown report, post Slack alerts for high scores. Required retries, error logging, strict TypeScript, no "any". Task 2: Real-time code review UI React + TypeScript, WebSockets, inline comment threads, optimistic updates with rollback, virtualized diff viewer, WS reconnect with exponential backoff. No UI libraries. Build from scratch. What Claude Code did: - Ran `/mcp` to verify tools before writing a line - Built 36 files in 12 minutes - Wrote an unprompted two-client WebSocket smoke test (broadcast: 3ms) - Zero "any", passed typecheck first try - UI worked immediately What Codex (via Cursor) did: - Failed Task 1: GitHub MCP wasn't reachable through Cursor's execution path. Handled it cleanly though: retried 3 times, logged errors, didn't crash. - Task 2 shipped a working UI in ~15 min, smoke test passed at 5ms - Hit TypeScript errors on first compile and an infinite React loop (useEffect calling hydrate repeatedly). Needed a ref guard patch. - 28 files, more compact architecture Cost (estimated, both tasks): - Claude: ~$2.50 - Codex: ~$2.04 About 18-23% difference. Not massive, but real. What I actually think: Neither agent "won". They're built for different things. Claude feels like pairing with someone who verifies everything before touching the keyboard. Codex feels like a senior dev who wants to ship and move on. What surprised me: no "any" leaks, no hallucinated tool names, both got WebSocket broadcast under 10ms. Six months ago that wasn't a given. submitted by /u/geekeek123 [link] [comments]

The Mundane Risk

The biggest near-term AI safety risks aren't dramatic — they're mundane. And that's precisely why they're neglected. This essay argues three things: (1) mundane AI failures are already causing measurable damage at scale, (2) current alignment approaches may depend more heavily on sandboxed environments than the field openly acknowledges, and (3) capability convergence and deployment pressure are making accidental open-world exposure increasingly plausible before robust ethical reasoning exists. (written with the help by Claude 4.6 Opus) The Atomic Bomb Before the atomic bomb existed, the risk of nuclear annihilation was 0%. Those who warned about the theoretical possibility were easily dismissed. Why worry about a risk whose preconditions don't even exist yet? In The Precipice, Toby Ord argues that when the stakes are existential or near-existential, even small probabilities demand serious attention. When the expected harm is so large, dismissing it on the basis of low likelihood is not caution but negligence. Before the bomb was built, the total risk of nuclear annihilation was absolutely 0%. Yet once it was invented, even a fraction of a percent justified enormous investment in prevention. The question was never "is nuclear war likely?" It was "can we afford to be wrong?" The same logic applies to AI. The preconditions for the next class of risk are visibly converging. And we're repeating the same pattern of dismissal that history has punished before. The Pattern As Leopold Aschenbrenner noted in Situational Awareness: "It sounds crazy, but remember when everyone was saying we wouldn't connect AI to the internet?" He predicted the next boundary to fall would be "we'll make sure a human is always in the loop." That prediction has already come true. Last year I argued how AI might accidentally escape the lab as a consequence of cumulative human error (for a vivid illustration of a parallel chain of events, I'd recommend the Frank scenario). At the time of writing, the argument that cumulative human oversight failures could compromise AI agents was dismissed as implausible: the consensus was that existing security protocols were sufficient. Months later, OpenClaw validated the structural pattern at scale. Not because the AI was misaligned, but because humans deployed it faster than they could secure it. It was clear: the failure modes from the Frank scenario could no longer be dismissed as simple fiction; it was now a structural pattern that OpenClaw validated in the real world. And this was all just with relatively simple autonomous agents. As capabilities increase, the same pattern of human excitement overriding security oversight doesn't go away – it gets worse – and because the agents are more capable, the failures also become a lot harder to detect. The numbers confirm this: [88% of organizations reported confirmed or suspected AI agent security incidents]() 14.4% of AI agents go live with full security and IT approval 93% of exposed OpenClaw instances reportedly had exploitable vulnerabilities [[MOU1]](#_msocom_1) Mundane risk pathways aren't hypothetical. They're already here in rudimentary form, and they're being neglected. We’ve known for a long time that existential risks aren’t just decisive, they’re also accumulative. And so far every safety breach has been mundane with systems operating inside their intended environments. No agent tries to escape on their own — their behaviour (like Frank’s) is usually a direct consequence of what they were deployed to do combined with accidental human oversight. So consider: if we can't secure the sandbox door with today's relatively simple agents, what happens when the systems inside are capable enough that a single oversight failure doesn't just expose a vulnerability? The capabilities required for autonomous operation outside the lab are converging on a known timeline. If AI were to leave the nest today, would it be prepared for an uncurated, messy world? Or would it be like the child and the socket? Current Alignment: Progress, But Fast Enough? Admittedly, the field is making real progress and Anthropic's recent publication "Teaching Claude Why" represents a real step forward. It was long suspected that misalignment doesn't require intent, just pattern completion over a self-referential dataset. But Anthropic has now traced one empirical pathway with findings consistent with the idea that scheming-like behaviour emerges from default priors in pre-training. Furthermore, their study also confirmed that rule-following doesn't generalize well, and understanding why matters more than simply knowing what. The significance of this is that it puts traditional alignment strategies into serious doubt and highlights the fundamental limits that current constitutional AI and character-based approaches still do not resolve. After all, we now have strong empirical evidence that behavioural alignment issues are most likely shaped by default prio

TabPFN-3 just released: a pre-trained tabular foundation model for up to 1M rows [R][N]

TabPFN-3 was released today, the next iteration of the tabular foundation model, originally published in Nature. Quick recap for anyone new to TabPFN: TabPFN predicts on tabular data in a single forward pass - no training, no hyperparameter search, no tuning. Built on TabPFN-2.5 (Nov 2025) and TabPFNv2 (Nature, Jan 2025), which together crossed 3M downloads and 200+ published applications. What's new: Scale: 1M rows on a single H100 (10x larger than 2.5).A reduced KV cache (~8GB per million rows per estimator) and row-chunked inference make this practical on a single GPU Speed: 10x-1000x faster inference than previous versions. 120x on SHAP via KV caching Thinking Mode (API only): test-time compute pushes predictions further via one-time extra fitting at inference. Beats every non-TabPFN method on TabArena by over 200 Elo, including 4-hour-tuned AutoGluon 1.5 extreme. Gap more than doubles to 420 Elo on the larger-data slice. Accuracy: it has a 93% win rate over classical ML on TabArena Many-class: native non-parametric retrieval decoder supporting up to 160 classes Calibrated quantile regression: bar-distribution regression head produces calibrated quantile predictions in a single forward pass Lifts adjacent tasks: time-series, interpretability, and new SOTA on relational benchmarks. 3 deployment paths: API, enterprise licensing, and open-source weights (permissive for research and academic evaluation) You can try it here or read the model report here. Happy to answer questions in the comments. submitted by /u/rsesrsfh [link] [comments]

Excel: Agent vs Plugin vs Human

I just found out that OpenAI released a plugin for Excel not too long ago. So I thought I would give it a spin. The same instructions were provided to AGENT and Plugin. To build a healthcare/public health analytics workbook in Excel (detailed script available if required. - Phase 1: Derive dataset by criteria. No links provided. - Phase 2: Clean data for excel processing with specified tabs. - Phase 3: Implement Python in excel. Key Takeaways: The Excel plugin usage derives from CODEX. While it has SOME smarts, it would be best suited for basic functionality if you want to prioritise your token usage. Unlike most coding AI, it has persistent task decomposition, retry/recovery behaviour (VERY COOL), Environment awareness, state tracking, self correction, execution failback strategies and verification passes It feels like a combination of agent+codex. I would lean more close towards describing it as orchestration behaviour. Slicer behaviour is a problem for both codex and agents. Just a limitation. https://preview.redd.it/y5cz4i0w8g0h1.png?width=3428&format=png&auto=webp&s=bd842e54c8a8e343e6a4aa9840b1ae53cc5e51f7 Verdict: I can still claim to reign supreme in ✨aesthetics✨, nested varying formulas and applying slicers. Agent is pretty good for a first pass with corrections from human after. OpenAI Excel plugin is okay for data pulls and creating formulas. Just don't expect anything too complex. submitted by /u/ValehartProject [link] [comments]

The US is spending more on data centers than on offices. Building housing for the new workforce.

submitted by /u/EchoOfOppenheimer [link] [comments]

Not a good day for team "Claude Mythos is Just Marketing Hype"

src - https://hacks.mozilla.org/2026/05/behind-the-scenes-hardening-firefox/ submitted by /u/EchoOfOppenheimer [link] [comments]

Not a good day for team "Claude Mythos is Just Marketing Hype"

src - https://hacks.mozilla.org/2026/05/behind-the-scenes-hardening-firefox/ submitted by /u/EchoOfOppenheimer [link] [comments]

Can Claude in Excel work for an in/out dashboard?

I’m wondering whether Claude in Excel can realistically be applied to automate my work. I’ve seen many YouTubers strongly recommending automation with Claude, but when I watched some videos to learn how to use it, I started to question how reliable it actually is. In one example, the user asked Claude to extract only the sales data from Jan to Mar from raw sales data and list it in a new sheet. However, the result included incorrect data from June, October, and December. What I need for my work is to consolidate the following data into a single sheet: - incoming/delivery schedules and quantities and purchasing plan by part number - Combine all datasets into one sheet and calculate inventory by date Currently, I spend about 3 hours per week creating this Excel file, and another person spends about 1 hour reviewing it, so about 4 hours in total. If Claude produces unreliable results, I expect it would take around 2 hours just to re-check everything, since I would need to review it more carefully than manually created data. While this could still save about 2 hours, if I have to assume that errors may appear in completely unexpected places every time I review the data, the mental fatigue might outweigh the time savings. For those who are actually using Claude in Excel, how accurate has it been in your experience? submitted by /u/RaccoonCheersYouUp [link] [comments]

Monthly releases of e-books on Amazon since ChatGPT

submitted by /u/EchoOfOppenheimer [link] [comments]

Anthropic: AI will fully replace software engineering by 2027. Also Anthropic: Currently hiring for 122 SWE openings.

I’m not playing a gotcha game here. AI is undeniably changing software engineering and I can’t think of a better AI use case than coding. But is AI replacing software engineering end-to-end? I’m not so sure. Anthropic’s own hiring trend tells a very different story than the AI replacement messaging Dario Amodei has been running. In fact, Anthropic’s software openings have seen a steady increase (184%) since Jan 2025. We’re shipping more software than ever. You’d think that means more engineers, not fewer. The industry signals point in that direction, too: - Amazon planning to hire 11,000 SWE interns in 2026 - NVIDIA claiming compute costs more than employees - SaaS reliability metrics down across the board (see GitHub) - AI coding tool pricing models currently unsustainable - Companies reporting no wide-scale AI productivity gains Software jobs are down big time since the 0-interest rate era and the recent “AI transformation” layoffs are real. It’s tough for engineers right now. My inkling is that’s a temporary setback, though. AI is here to stay. But so are software engineers. - Joel Griffiths submitted by /u/ImaginaryRea1ity [link] [comments]

Is Codex the best right now?

Why are so many people downloading Codex now? submitted by /u/LeTanLoc98 [link] [comments]