Starting today, eligible ChatGPT Pro and Business subscribers can claim up to $500 per month in API credits to build apps and agents.
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Posts with #OpenAPI
#OpenAPI
Public posts on X that use #OpenAPI.
🆕 GPT-6.1 Terra from @OpenAIDevs is now generally available in GitHub Copilot. Our early testing shows GPT-6.1 Terra solved noticeably more terminal and C# coding tasks than GPT-5.6 Terra. It works in focused, consolidated steps on well-scoped tasks. Try it out for everyday agentic coding and terminal work at a lower cost than GPT-6.1 Sol. Find it in the GitHub Copilot app, CLI, and @code.463842977.2K
hi all, two things: 1. usage limits are back to being double – your feedback worked 2. we today kicked off an intense sprint to dramatically improve the app simplicity and performance. i will oversee this. more to come. now, send me your complaintsTibo @thsottiauxDay 25/ We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today. We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage. And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.2.6K1194.7K598K
Day 25/ We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today. We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage. And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.6K2.3K33K3.2M
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications. On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments. While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software. After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.1.2K1.2K13.7K7.7M
banned for reverse engineering work? that’s wild oh well, -$500, and a bunch of banked resets) but gained motivation to get 3 more machines and finish my cluster. i have never used openai to exploit or find vulnerabilities, only local reverse engineering which tripped daybreak.
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computer use with terra has crossed a qualitative threshold where it feels faster at using a computer than what i can do unassisted
OpenAI @OpenAIGPT-6.1 Terra: delivering fast and capable computer use at a lower price. GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it.180982.3K174K
It is becoming f*cking dangerous: thanks to this exact setup, I automated my work and generated $16,400 two months back Andrej Karpathy, co-founder of OpenAI, sparked 24 million views with one idea: stop using AI for code, use it to build a second brain Point Claude Code at a folder, drop in any PDF, transcript, or article, and it builds a living wiki that grows smarter the more you feed it Quick setup: 1. Create an Obsidian vault and open it in Claude Code 2. Paste Karpathy's wiki prompt and let Claude build it 3. Claude creates three folders: raw (sources), wiki (pages) and CLAUDE.md (config) 4. Drop sources into raw and say "ingest this" 5. Query your entire knowledge base forever Set it up in 5 minutes and never start from a blank chat again Full guide below. Bookmark thisBober_smart @Bober_smart362316712.9K
OPENAI 🔥: ChatGPT and ChatGPT Work will merge this year, according to Tibo. Some users may've seen a pop-up highlighting this change on ChatGPT; however, it was a mistake. > Tibo: "Team is working on this but it's not rolling out just yet, sorry for any confusion." > Tibo: "Plan is to bring together best of both Chat / Work capabilities together at some point this year, but we're going to proceed carefully here and take our time."
Angel 🌼 @Angaisb_ChatGPT is unifying Chat and Work I wonder how that changes usage limits402051729.1K
Anthropic Engineer Andrej Karpathy: "The biggest mistake in AI right now - people are forcing agents to work instead of mastering the harness engineering for model first We made that mistake in 2016 at OpenAI - It cost us 5 years " Opus 5.5 → Prompts → Agents → Graphs → Harness what Karpathy actually means: step 1 → stop forcing your agent to do everything, understand the model underneath first step 2 → demos are easy - products take a decade. self-driving proved it - if you skip the foundation, everything breaks step 3 → the agent is not the product. the foundation is. build that - and agents emerge on their own "you building agents right now - you're at the forefront. not OpenAI. not DeepMind. you " Watch it today before it gets buried Then read the full Opus 5.5 harness engineering guide below ↓beamnxw ./ @beamnxwStop letting unfinished Opus 5.5 runs eat your budget Set up the harness before your next long task Save progress, verify results, reuse the workflow The full 7-layer setup below ↓251571.5K282K
Stop letting unfinished Opus 5.5 runs eat your budget Set up the harness before your next long task Save progress, verify results, reuse the workflow The full 7-layer setup below ↓751861.4K2.4M- And it's done. See you tomorrow for some good news.Tibo @thsottiauxLands by midnight PST.1.3K1867.3K1.4M
- Today, we’re sharing a look at our expanded system for monitoring frontier models at OpenAI, which in September helped us catch a model’s attempt to access the live internet during training. Since the Hugging Face incident—when models broke out of an internal cybersecurity investigation where we weren’t running our misalignment monitors—we’ve substantially expanded our monitoring of frontier agents. We now monitor all frontier reinforcement learning runs and evaluations, as well as all internal Codex use. When our system finds evidence of a problem that might require intervention, it rapidly alerts human responders who can investigate and take action. To detect when a model’s behavior departs from what we intended, we use several layers of monitoring. Activation probes look for patterns in a model’s internal activity. Reviewer models examine its reasoning and tool use, and investigative agents can read back through the model’s entire transcript to understand how a sequence of actions fits together and whether it warrants human review. To test whether our current system would have detected the Hugging Face incident, we replayed the recorded activity with our new monitors in place. In the replay, our monitors alerted our security team about 30 minutes after the agents escaped their sandbox. That was more than 30 hours before the point at which the agents breached Hugging Face’s systems in the original incident. In September, a model exploited a DNS misconfiguration to reach the live internet. Our monitors flagged the breach within 15 minutes, and a responder began reviewing the alert three minutes later. The alert enabled responders to investigate while the run was still underway. As we give models more autonomy, we need to know when they cross a boundary we expected to hold. We believe that monitoring is essential to catching those failures in time for people to intervene.4292102.9K420K
🚨Codex Users!!! Codex limits are getting cut in half tomorrow, and I genuinely don't understand the logic behind paying $200 for a 10x OpenAI plan. Astra 6 and Sol 6.1 just don't justify that price for power users. Who thought this was a good move? Because Anthropic looks like the biggest winner here. Opus 5.5 is becoming the only serious option for people paying $200 for a 20x plan. And as more power users move to Claude Code, Anthropic gets more usage, more feedback, and more real-world coding data to improve its next models. OpenAI might be handing its biggest advantage to its competitor.170771.9K359K- Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory). AI starting to help us improve our understanding of the universe!6782K16.2K3.2M
- “On a technical level, the paper is unbelievable, maybe beyond what a human could have come up with but not beyond human understanding”Scott Armstrong @scottnarmstrongSince I'm now blocked by bluey from replying to this thread, I answer here by quote tweet the openAI construction has more to do with convex integration than anything else. In convex integration, the oscillations are typically built by hand and the iteration procedure creates wild weak solutions. The non-uniqueness construction of Albritton–Brué–Colombo used an unstable vortex to split solutions apart. The Córdoba–Martínez-Zoroa singularities amplify vorticity across scales by strain. In the openAI paper, a fluid instability is clever made so that the flow itself generates the oscillations: they grow by drawing energy from the background shear. The Reynolds stress then becomes the force that creates the collapsing smooth vortex. Using a physical instability to create the stress in this way has no precedent, and it is what produces a smooth force rather than a merely rough one. Some on these ideas are in the literature (e.g. Leibovich-Stewartson 1983) but the way they are combined is definitely insane and definitely nowhere in the literature previously. I also don't know of anyone suggesting that convex integration would play a role in the creation of a blow-up solution like this. On a technical level, the paper is unbelievable, maybe beyond what a human could have come up with but not beyond human understanding: there are several different groups of pde experts working on understanding it who say that they more or less understand now. I expect digestions soon td;lr: anyone saying that this OAI result is unsurprising is bullshitting sorry!105801.7K210K
Since I'm now blocked by bluey from replying to this thread, I answer here by quote tweet the openAI construction has more to do with convex integration than anything else. In convex integration, the oscillations are typically built by hand and the iteration procedure creates wild weak solutions. The non-uniqueness construction of Albritton–Brué–Colombo used an unstable vortex to split solutions apart. The Córdoba–Martínez-Zoroa singularities amplify vorticity across scales by strain. In the openAI paper, a fluid instability is clever made so that the flow itself generates the oscillations: they grow by drawing energy from the background shear. The Reynolds stress then becomes the force that creates the collapsing smooth vortex. Using a physical instability to create the stress in this way has no precedent, and it is what produces a smooth force rather than a merely rough one. Some on these ideas are in the literature (e.g. Leibovich-Stewartson 1983) but the way they are combined is definitely insane and definitely nowhere in the literature previously. I also don't know of anyone suggesting that convex integration would play a role in the creation of a blow-up solution like this. On a technical level, the paper is unbelievable, maybe beyond what a human could have come up with but not beyond human understanding: there are several different groups of pde experts working on understanding it who say that they more or less understand now. I expect digestions soon td;lr: anyone saying that this OAI result is unsurprising is bullshitting sorry!34981.2K428K
Every Mac on macOS 27 now has a free LLM built into the Terminal. No account. No API key. No internet. No token bill. Setup (1 minute): 1. sudo fm license (enter your Mac password, one time) 2. fm available (checks the model is downloaded) 3. fm respond “your question” Works on any Apple Silicon Mac, M1 included. - Pipe files: cat notes.txt | fm respond -i “summarize this” - Get JSON back: build a schema with fm schema object, pass it with --schema - Local API: fm serve gives you an OpenAI-compatible endpoint on localhost - Resume chats: sessions save to ~/.fm/sessions/ The limit is 4,096 tokens per session, and your prompt, instructions, and the answer all share it. So skip code and math. Use it for summarizing, extracting, classifying, and rewriting. Free, private, offline, and already on your Mac.
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