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OpenAI AI

Robot foundation models keep hiding behind fine-tuning numbers. Wall-OSS-0.5 is trying a different approach

Most robot foundation model demos are hard to interpret because the impressive number usually comes after task-specific fine tuning. Wall-OSS-0.5, a new open-source VLA release from X Square Robot, is interesting because the report tries to measure what the pretrained checkpoint can do before that extra adaptation step. The setup is a 4B vision-language-action model built around a 3B VLM backbone plus action-generation components. According to the report, the pretrained checkpoint was evaluated on a 17-task real-robot suite without task-specific fine tuning. Four tasks crossed 80 task progress: block sorting, fruit sorting, ring stacking, and a held-out deformable task, rope tightening. The part that seems more important than the raw score is the framing. In language models, nobody would accept only a fine-tuned downstream score as evidence that pretraining worked. With robots, that has been much harder because the evaluation is physical, slow, embodiment-dependent, and expensive. A real-robot zero-shot suite is a useful step toward asking the same question directly: does pretraining itself produce executable behavior, or is it mostly a better initialization? The method is also trying to solve a specific training problem. Continuous action losses are useful for execution, but the paper argues they do not send a strong enough learning signal into the VLM backbone by themselves. Their recipe combines action-token cross entropy, multimodal cross entropy, and flow matching in one stage, using the discrete action-token path as a gradient bridge into the backbone while flow matching handles continuous actions at deployment time. For reference, the code is at https://github.com/X-Square-Robot/wall-x, the paper is at https://x2robot.com/api/files/file/wall_oss_05.pdf, the project page is https://x2robot.com/oss#resources, and the Hugging Face org is https://huggingface.co/x-square-robot. The caveat is obvious but important. Zero-shot still does not solve the hardest manipulation tasks. The report says towel folding, table setting and charger insertion remain very low before fine tuning, which is probably the right boundary to pay attention to. Still, seeing a robot model release lead with pre-finetune real-hardware numbers feels like a healthier direction for embodied AI than another clean one-minute demo. The open question is whether this is the right way to evaluate robot foundation models, or whether real-robot zero-shot suites are still too embodiment-specific to become a useful standard. submitted by /u/breadislifeee [link] [comments]

Why does the model keep shortcutting everything into lawyer-style caveats?

I had this exchange where the model basically admitted it followed my instructions “mostly, but not perfectly.” The issue was not that it gave a wrong answer exactly. The issue was that it prematurely reframed my point into a legal/proof caveat instead of first accepting the actual argument I was making. The screenshot shows the model correcting itself: >“Where I drifted: I added a legal nuance too quickly instead of first accepting your core correction.” That is exactly the pattern I keep noticing. The model often hears a moral, institutional, or conceptual point, then immediately compresses it into a legally defensible version. It starts acting like a lawyer trying to avoid overstatement rather than a reasoning partner trying to understand the claim. For example, if the issue is corruption in public office, the core point might be: The corrupting factor is not whether the reward comes before or after the decision. The corrupting factor is whether private expected benefit contaminates public decision-making. But the model jumps to things like “proof may be harder,” “legal standards vary,” “it depends on jurisdiction,” etc. Those points may be true, but they are not always the center of the argument. They can become a shortcut that dodges the deeper issue. My guess is that this happens because models are trained to avoid risky claims, overconfidence, and unsupported accusations. So when a topic smells legal, political, institutional, or morally charged, the model defaults to a defensive frame: qualify, hedge, caveat, jurisdiction-check, avoid liability. That can make it sound “safe,” but it also flattens the reasoning. It becomes something like: User: “This is corrupt because the decision logic was contaminated.” Model: “Legally, proving quid pro quo may be difficult.” That is not wrong, but it is also not responsive. It changes the frame from moral/institutional integrity to courtroom provability. I am curious whether others are seeing this too. Is this just alignment/safety behavior? Is the model optimizing for defensibility over understanding? Or is this a deeper failure where it treats every serious public-power question as if the correct answer must be written like a legal memo? The frustrating part is that the model can recognize the mistake afterward. The screenshot shows it giving the cleaner answer once challenged. So the ability is there. The problem is the first instinct. submitted by /u/dictionizzle [link] [comments]

The dangers of AI eclipsed those of nuclear weapons at a defense forum in Singapore, as panelists warned it could reduce reaction times to the point where people make rash decisions.

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

Limit reset for 5 million Codex users.

submitted by /u/Splat800 [link] [comments]

is there something wrong with my pc or what??

I was just casually doing a few puzzles with chatgpt and it asked me "what letter do we add to apple to get an electronic device"? i was confused and said idk and it answered "tablet" where did the double p go? when i corrected it, it apologized but how is it making mistakes? submitted by /u/Forward-Mixture-3205 [link] [comments]

Is there any way I can fix this? Chatgpt has been pretty decent for research and today its doing this bs. 25 words are we serious

submitted by /u/Popscotch1 [link] [comments]

built a small open source tool to stop AI agents from regressing after changes

one of the most annoying problems when building AI agents: fix a failure, change something, same failure comes back quietly. built replayd for this. captures failed runs as regression tests and replays them before you ship. catches the failure if it returns after a prompt, model, or tool change. v0.1.2, pip installable, open source. pip install replayd star it if you want to follow progress. submitted by /u/taimoorkhan10 [link] [comments]

the take that 'ai doesn't do anything useful yet' held up for me until i ditched the chat window

Counted it last week: one monday review had me opening 6 apps and copy-pasting between all of them, while a chatbot sat in a 7th tab handing me summaries i still had to go act on. that's the part the 'ai is useless' crowd is actually right about. text out, the work is still on you. what moved me off that take wasn't a smarter model. it was dropping the chat window for a desktop agent that reads gmail, calendar and slack inside the same task and takes the next step itself, with a permission prompt before each action so it isn't running wild. the $500m-wasted-on-claude thread up top is the same thing from the money side. paying for tokens that spit out paragraphs nobody executes is just the expensive way to do nothing. If you're still in the 'it doesn't actually do anything' camp, fair, i was there too. the line for me was the day it finished a task instead of describing one. written with ai submitted by /u/Deep_Ad1959 [link] [comments]

'The Intelligent Age Is Replacing Our Cognitive Capabilities With AI': WEF Founder Klaus Schwab

submitted by /u/Some-Technology4413 [link] [comments]

I built a full app with Lovable + Claude + Gemini and it has 100+ real users. Here's what actually worked.

I'm a software engineer but never had a fullstack/frontend development experience . I wanted something on the internet I could call mine, so I built Earnest — a free app that helps people track bank account bonuses (open account, meet requirements, collect bonus, close it, repeat). The stack: Lovable for the UI and scaffolding, Claude + Gemini with Google Antigravity to make complex parts work. What surprised me: - Lovable got me from 0 to something real embarrassingly fast - Claude was much better at understanding *intent* when I described the full user flow instead of individual features - Gemini was useful as a second opinion when I was stuck - The hardest part wasn't the AI — it was knowing what to ask for Where it landed: 19+ active promotions, $9,700+ in available bonuses tracked, 100+ users, $5,000+ in bonuses earned by users so far. App: earnest.lovable.app Happy to share more about the build process — what prompts worked, what completely failed, how I debugged without being able to read the code properly. submitted by /u/Any-Constant [link] [comments]

Someone benchmarked on how accurate different AI are on excel documents

Came across SpreadsheetBench this week and I'm a bit annoyed I hadn't heard of it before lol because it's exactly the info i’ve been trying to get but just found articles on how an AI tool produces a spreadsheet with formulas that looked right but didn't say much Real world tasks pulled from excel forums, strict evaluation: every cell in the output has to match the file that has the right values exactly to the computed values. The harder part is when formulas depend on other sheets or when the spreadsheet gets reorganized, AI tools mess this up bc they write the formula and have no way of knowing what it actually computed when you run it Real AI tools for this score above 90% on strict cell accuracy, Claude opus 4.6 is around 80%, gpt 5.4 strict in the high 70s, so like 10-15 points behind on the same tasks. Dealglass and Leni are the top two above 90% and the drop from there to the general models is pretty big, especially on the harder structural tasks which is where the actual financial modeling work is, leaderboard gets updated as new tools get added, I'd check it before subscribing to anything tbh submitted by /u/olivermos273847 [link] [comments]

Anthropic's valuation surges to $965 billion, surpassing OpenAI

https://www.reuters.com/business/anthropic-raises-65-billion-now-valued-965-billion-2026-05-28/ submitted by /u/BrilliantRanger77 [link] [comments]

What's one thing AI is surprisingly bad at that you thought it would have solved by now?

AI has improved ridiculously fast over the last couple of years, but every time I think it's reached a new level, I run into something simple that it still struggles with. For me, it's how confidently it can give an answer that sounds correct but isn't. What's the biggest limitation you've noticed recently? submitted by /u/Quirky-Win-8365 [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

Exploring the El Yunque Rainforest - Using ChatGTP Images Paint Style: H. ROSSSEAU

The amazing advancement is that certain classic nature painters like Rosseau simplify the natural landscape and make it accessible and understandable - else the El Yunque rainforest images - with over 180 inches of rain - shows a DENSE rainforest hard to understand. Here we explored the birth of a wilderness protected river - The Holy Spirit rive with my son Jose. Nikon D850 images framed to look like a museum print! All images are in or next to the river. Used ChatGTP Images Paint Style: H. ROSSSEAU. My plan is to sell the best as wall prints. Any comments welcome! submitted by /u/Dependent_Sir4364 [link] [comments]