Codex coding agent and its ecosystem
Codex capabilities, use cases, internal OpenAI adoption, enterprise workflows, the Claude Code plugin, open-source forks, and the maintainer credit program.
24%
Best tweets about OpenAI
50 Best Tweets About OpenAI (2026)
Explore the best tweets about OpenAI, from model and API releases to research, developer tools, and real-world applications. Updated every two weeks.
Substantive OpenAI product, research, and developer discussions rather than generic mentions of artificial intelligence.
- Creators
- 46
- Updated
Original Xholic analysis
What 50 top OpenAI posts reveal
This X conversation is substantive rather than generic: 50 posts from 46 creators concentrate on specific OpenAI products and decisions, with Codex as the largest of several major themes (24% share), followed by business economics and monetization (14%), company history and internal transformation (14%), consumer agents and the super app (14%), and developer platform resources (10%). Stance toward OpenAI is uniformly SUPPORTIVE (100%), while sentiment is 46% NEUTRAL, 36% POSITIVE, 10% MIXED, and 8% NEGATIVE. Engagement is outlier-driven: the top post runs 60.01x the median all-time score, and the overall median all-time score is 27.35. Most posts are older — median age is 145.9 days, with 13
- Dominant tone
- Neutral
- Median score
- 27.4
- Leading format
- Announcement
- Recent posts
- 26%
46% of posts
All-time engagement
100% of posts
Published in 90 days
Conversation map
The themes creators return to
Business model, compute constraints, and monetization
Revenue growth, subscription tiers and usage-limit cuts, compute bottlenecks, advertising, spend/market-share data, custom chips, cloud partnerships, and IPO-era financial pressures.
14%
Company history, culture, and internal transformation
Origin stories (GPT-3 API decision, Musk, early AGI memos), research-vs-product culture debates, org restructuring, and how agents are reshaping work inside OpenAI itself.
14%
Super app, consumer products, and always-on agents
DevDay launches (dots/Aeon), the ChatGPT–Codex merger, proactive persistent agents, marketplace and sign-in infrastructure, voice products, and the consumer personal-assistant vision.
14%
Developer platform, API, and learning resources
OpenAI API, Cookbook, Agents SDK, Evals, prompt guides, data-sharing API credits, enterprise platform security (MCP, identity federation), and curricula like OpenAI Academy.
10%
Automated AI research and recursive self-improvement
OpenAI's automated researcher ambitions, RSI safety hiring, transparency data on models accelerating research, and long-horizon AGI roadmaps.
8%
Competitive landscape: OpenAI vs Anthropic vs open source
Strategic contrast between OpenAI's consumer orientation and Anthropic's enterprise motion, competitive switching dynamics, and OpenAI's embrace of open-source models as a platform play.
8%
New model releases and hands-on evaluations
GPT family releases (Sol, Terra, Luna, Astra, Spud rumors), first impressions, prompting behavior changes, and head-to-head comparisons with Anthropic models like Opus and Fable.
8%
Tone and stance
SentimentNeutral leads
Author postureSupportive leads
Performance benchmark
- Median likes
- 141
- Median reposts
- 13
- Median replies
- 15
- Median views
- 15.8K
Posts with media make up 68% of this collection. Their median all-time score is 25.1, compared with 59.2 for text-only posts.
Format mix
- Announcement100% · score 27.4
Consensus and debate
Where creators agree, and where they do not
Shared view
Codex is the conversation's center of gravity
The Codex coding agent and its ecosystem account for 24% of posts (12 tweets). Posts describe internal adoption across departments, a published use-case catalog spanning engineering, QA, security, and life sciences, and an internal tooling explosion enabled by Codex.
Shared view
OpenAI and Anthropic are seen as pursuing different market motions
Multiple posts frame OpenAI as consumer-first (ChatGPT distribution, personal agents, hardware ambitions) while Anthropic is framed as enterprise- and workflow-oriented, with revenue-recognition differences making direct comparisons misleading.
Shared view
Compute constraints are tied to subscription changes
Discussion of usage-limit reductions attributes them to severe compute constraints rather than malice, and notes the framing of the cuts as an efficiency improvement was received skeptically by at least one prominent commenter.
Open debate
DevDay always-on agent (Dots/Aeon) reception is split
One DevDay attendee describes Dots as a significant step toward proactive, always-on assistance, while another DevDay reaction calls the always-on agent the headline announcement yet 'not particularly compelling' and criticizes the $200/$500 tier restructuring as the end of subsidization.
Open debate
Model quality vs. rivals is contested
Hands-on evaluations disagree on standing against Anthropic models: one early-access post calls a GPT model the most pleasant OpenAI model tested but still behind Fable on frontend design, while another first-impressions post says Fable remains the king of raw intelligence but GPT-5.6 models are the better daily drivers on price and speed.
What performs
Patterns behind standout posts
Engagement is heavily outlier-driven
The top post scores 1641.35 (60.01x the median of 27.35), and the top five outliers span 11.09x to 60.01x. Median engagement is modest: 141 likes, 15,809 views, 15 replies, and 13 reposts.
Text posts outperform media posts on median score
Despite 68% of posts carrying media, the median all-time score for text posts (59.19) is more than double that of media posts (25.06), suggesting long-form argument and analysis drove the highest engagement in this set.
Freshness is limited
Only 26% of posts (13 of 50) were published within 90 days; the corpus spans from early February to early October 2026 with a median age of 145.9 days.
Statistical standouts
- View standout post 1Score 1641.3 · 60.01× median
- View standout post 2Score 1073.3 · 39.24× median
- View standout post 3Score 447.2 · 16.35× median
- View standout post 4Score 428.9 · 15.68× median
- View standout post 5Score 303.4 · 11.09× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 18% of the selected posts.
1. Chris
@ChrisGPT
2 posts
2. Machina
@EXM7777
2 posts
3. Mark Kretschmann
@mark_k
2 posts
4. Vaibhav Sisinty
@VaibhavSisinty
2 posts
5. Activate
@ActivateSignal
1 post
6. AI Highlight
@AIHighlight
1 post
A long tail of creators with a few high-leverage voices
46 creators produced the 50 posts, and the top five accounts account for only 18% of placement — the conversation is broadly distributed rather than dominated by a handful of accounts.
The single highest-engagement post came from a low-volume creator
Alex Veremeyenko (alex_verem) posted once in this set but holds the highest per-post score in the corpus (1073.32) with a long-form prompting-guides comparison.
Recurring voices concentrate on specific themes
Mark Kretschmann (mark_k) appears twice, both on high-scoring Codex use-case and automated-researcher posts (median all-time score 132.57 across his posts in the set); Chris (ChrisGPT) and Machina (EXM7777) each post twice on automated research / RSI hiring and model evaluations respectively.
Since the previous snapshot
What changed since Oct 14, 2026
- 80% of the selected posts remained.
- The creator count changed by +3.
- The leading sentiment moved from Positive to Neutral.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best OpenAI tweets
Top OpenAI tweets from 46 creators
Ranked 01–50
- 01
@kliu128 ·
Today we're releasing data on models accelerating research at OpenAI. Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same. https://t.co/iLKbrLcBAI
- 234Replies
- 697Reposts
- 6.4KLikes
- 2.4MViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 02
@alex_verem ·
Both OpenAI and Anthropic just released official prompting guides. Both say the same thing. Your old prompts don’t work anymore. But for opposite reasons. Claude Opus 4.7 stopped guessing what you meant. It does exactly what you type. Nothing more, nothing less. Vague instructions that worked on 4.6? They now produce narrow, literal, sometimes worse results. Not because the model got dumber. Because it stopped compensating for sloppy thinking. GPT-5.5 went the other direction. OpenAI’s guide literally says: “Don’t carry over instructions from older prompt stacks.” Legacy prompts over-specify the process because older models needed hand-holding. GPT-5.5 doesn’t. That extra detail now creates noise and produces mechanical output. Claude got more literal. GPT got more autonomous. Both now punish the same thing: prompts written without clear thinking behind them. One developer on Reddit captured it perfectly after analyzing hundreds of community posts. The complaints tracked almost perfectly with prompt specificity. Precise prompts got better results on 4.7. Vague prompts got worse. The model didn’t regress. The prompts did. OpenAI’s new framework is “outcome-first prompting.” Describe what good looks like. Define success criteria. Set constraints. Then get out of the way. The model picks the path. Anthropic’s framework is the inverse: be surgically specific about what you want, because the model won’t fill in your blanks anymore. Two different architectures. Two different philosophies. One identical conclusion: the person writing the prompt is now the bottleneck, not the model. Boris Cherny, the engineer who built Claude Code, posted on launch day that even he needed a few days to adjust. That post got 936 likes. Meanwhile, Anthropic increased rate limits for all subscribers because the new tokenizer uses up to 35% more tokens on the same input. The model is more expensive to run lazily. Cheaper to run precisely. The models are converging in capability. The gap between good and bad output is no longer about which model you pick. It’s about the 2 minutes of structured thinking you do before you type anything. That thinking system is the skill. The prompt is just what it produces.
- 118Replies
- 272Reposts
- 2.3KLikes
- 332.2KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 03
@GergelyOrosz ·
Super interesting detail from inside OpenAI: The company hired a bunch of ex-Meta people. Some of these people wanted to invest heavily into internal tooling teams (Meta did it, worked great) OpenAI leadership resisted, saying that in an AGI-first world, there will be no internal tooling teams. And they were right: now, with Codex, there's a massive internal tools explosion, without having any internal tooling teams!
- 153Replies
- 146Reposts
- 4.9KLikes
- 858.5KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 04
@OpenAI ·
Work at OpenAI is being transformed by agents, in every department. Across our entire company, people are using Codex to do work that is more complex, longer-running, and increasingly cross-functional. Our internal usage offers an early look at how agentic tools may reshape work as they become more capable and broadly available.
- 430Replies
- 457Reposts
- 5.5KLikes
- 1.3MViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 05
@kunchenguid ·
ok here's my key takeaways from OpenAI DevDay keynote 1. dots grok bot, muse, and now dots - they may look similar, but they are not i think openai's dots is a significantly more cohesive implementation that spans across personal and business use, and also across single player and multiplayer pretty excited to see where it goes 2. sol 6.1 biggest thing? openai caught up on cheaper cached tokens. this is a big deal and affects the overall cost of long horizon work a lot in terms of sol the model itself, honestly it's bit weird that 6.1 is released only a few days after 6, making it feel like sol 6 was a premature release and 6.1 is what's supposed to be sol 6 all along we'll just have to put our hands on it to see how big of a jump it is 3. jev clone on one hand, it's incredible to see how quickly they cloned it. but on the other hand, it's scary to see how these frontier labs would not blink an eye to destroy smaller startups in their space openai's decision model already has image understanding which jev doesn't. it speaks to the moat openai has accumulated which makes it hard for startups to disrupt 4. ultrafast 8x speed at 6x price, only on astra right now but said to be coming for sol as well i don't know... how many use cases can actually get a positive ROI using astra at 6x price? 5. $500 subscription with the adjustment to the $200 plan and the introduction of $500, it's become clear that subsidization is coming to an end $200 is simply 10x of what $20 gives, and $500 is simply 25x - zero discount at these higher price points i'd rather they create $100 "packs" that you can stack onto your account 6. sign-in with chatgpt, and marketplace this is a BIG DEAL!!! why? because it means other companies can finally build agentic products without having to sell tokens selling tokens is a very bad business model for the vast majority of SaaS companies. it complicates their pricing structure and creates almost no profit margin at all this move from openai means customers just buy tokens from openai, and use them everywhere - very good for the ecosystem the impact to the broader industry will be amplified if anthropic gets pressured to follow the same pattern alright - might share more thoughts after actually putting my hands on the new things, but those are the initial reactions for now. curious to hear what you all took away from it as well!
- 74Replies
- 91Reposts
- 1.4KLikes
- 167.5KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 06
@alexxubyte ·
How OpenAI Built Its Data Agent Most teams building data agents stack routers, fine-tunes, and complex retrieval pipelines on top of multiple LLMs. OpenAI didn't. Their data agent runs on a single model and only 13 tools, across 1.5 exabytes and 90,000 tables. It's "pretty vanilla" by design. We spoke with Emma Tang, Head of Data Platform Engineering at OpenAI, to better understand the architecture and the engineering decisions behind it. The article covers: - The architecture behind the data agent - The six layers of context that make a single LLM reliable across 90,000 tables - How OpenAI Uses Codex Internally: 3 Use Cases - Five practical lessons for any team building a domain agent - Where OpenAI's data platform is headed next
- 15Replies
- 111Reposts
- 584Likes
- 45.6KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 07
@mark_k ·
OpenAI just published a new Codex use-case page, and it’s basically a catalog of what teams are already handing over to coding agents: engineering work, product work, QA, security, data analysis, internal tools, and even life-sciences workflows. Some of the coolest examples: ⬩ Reviewing GitHub PRs and understanding large codebases ⬩ Turning screenshots or visual references into responsive UI ⬩ QA-testing apps by clicking through real user flows ⬩ Refactoring legacy code, running migrations, and fixing vulnerability backlogs ⬩ Drafting PRDs, analyzing datasets, building internal apps, and assisting life-sciences research This is what coding agents look like when they stop being a demo and start becoming part of daily work.
- 31Replies
- 62Reposts
- 705Likes
- 48.3KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 08
@kimmonismus ·
Some thoughts regarding the current satiation with OpenAI. OpenAI is clearly in a difficult position right now, especially as compute capacity is becoming an increasingly serious problem. We recently saw new sign-ups for the $200 subscription temporarily suspended. Shortly afterward came the major blow: usage rates were effectively cut in half across the $200 plan and all the other plans, with GPT 6.1 Sol introduced as a highly efficient alternative to compensate. The usage allowances were generous at first, but inference was slow. It has since become faster, but it still has a way to go. Personally, I still prefer GPT 6 Astra on Medium, but with the reduced subscription limits, I will soon be unable to use it as much as I normally do. Worse still, I won’t be able to use future top-tier models in the Astra family to the same extent either. I don’t think this is down to malice on OpenAI’s part, but simply to how severe its compute constraints have become. Somewhat bafflingly, the headline announcement at Dev Day was the always-on agent, which I have yet to find particularly compelling. That makes it easy for Anthropic to position itself as the appealing alternative right now. I still consider Opus 5.5 the better model, particularly when compared directly with GPT 6.1. With Fable 5.5 due to be released soon, OpenAI is under even more pressure to respond. Honestly, I no longer understand OpenAI’s vision. By reducing its subscription allowances, OpenAI has made it easy for Anthropic to present itself as the better alternative and draw users back to Claude ahead of Anthropic’s IPO. OpenAI has done itself a real disservice. OpenAI’s recent communication has also been far from good. Instead of speaking plainly about the problems, it framed the reduction in weekly usage limits as an efficiency improvement, as though users were actually getting more than before. I honestly don’t believe that. Which leaves me wondering: Quo vadis, OpenAI?
- 166Replies
- 59Reposts
- 1.4KLikes
- 124.9KViews
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@ajassy ·
Excited about our new strategic partnership with @OpenAI. Developers and companies of all kinds are eager to run services powered by OpenAI models on AWS, and our unique collaboration will provide a stateful runtime environment for them that’s powered by OpenAI’s frontier intelligence on Amazon Bedrock. OpenAI is also going big on our custom Trainium chips, which are 30-40% more price performant than comparable GPUs, to power their growth. Both the leading AI labs now have made significant commitments to Trainium, which is gaining a lot of momentum. We’ll be the exclusive 3P cloud distribution provider for OpenAI Frontier (which enables organizations to build, deploy, and manage teams of AI agents). And finally, we’re excited about our investment in OpenAI — it’s an extremely talented team with great products, IP, and vision for how they can continue to serve customers and enterprises. We think they’ll be one of the big winners in AI, we can help them grow, and we believe we’ll earn a strong return for Amazon over the long term. https://t.co/aDJ8On5iQQ
- 188Replies
- 323Reposts
- 2.7KLikes
- 532.6KViews
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@rauchg ·
Spend of OpenAI vs Anthropic vs Open (Vercel AI Gateway, last 2 months) • Anthropic still #1 in spend, but went 69% → 40% • OpenAI: 10% → 24% in spend • GPT-6 Astra + GPT 5.6 Sol are ripping • OpenAI now leads in tokens # • Kimi K3 + DeepSeek took ~half of Anthropic's loss • Opus 5.5 is up to 10% of spend in 2 days • OpenAI is 62% of image generations Watch here: https://t.co/Gu7D9d7M8w
Watch video- 171Replies
- 52Reposts
- 895Likes
- 208.4KViews
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- 11
@StartupArchive_ ·
Sam Altman on the Paul Graham advice that saved Open AI: “Always make an API” Four years into OpenAI, Sam Altman and the team realized that they would have to build a really big company to fund the development of their increasingly capital-intensive foundation models. “We had this model called GPT-3,” Sam recalls. “I was turning up the urgency on the company to try and figure out a product, and we just couldn’t. It was cool, but it wasn’t good enough to make something that worked.” Then Sam remembered a piece of advice from Y Combinator founder Paul Graham that stuck with him: “You should always make an API. No matter what, you should make an API. Good stuff will happen.” Out of ideas for a product, the OpenAI team decided to make GPT-3 available as an API. “Maybe somebody will figure out something to do with it,” Sam thought. A few copywriting applications like Jasper and Copy AI did take off using the GPT-3 API, but OpenAI also noticed interesting behavior that eventually became a sleeper hit: “Some people — not a lot — would just chat with that thing all day,” Sam explains. “It wasn’t very good but there was clear user signal that people wanted to talk to the models. And given that that was the only thing besides copywriting that had real traction, we said, ‘Maybe this is just he product we should build.’” On November 30, 2022, ChatGPT was released to the public as a “research preview” using a model from the GPT-3.5 series. It reached over a million users in five days. Source: @khoslaventures (Sep 2025)
Watch video- 15Replies
- 31Reposts
- 427Likes
- 63KViews
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@markchen90 ·
How does OpenAI balance long-term research bets with product-forward research fundamentals? I’ve been getting this question a lot lately, usually framed as a suggestion that Jakub (@merettm) and I are pushing an increasingly product-focused agenda. That characterization is simply wrong. Foundational research has been core to OpenAI from the start, and today we run a research program with hundreds of exploratory projects - much like the ones that led to our reasoning-model breakthrough. The majority of our compute is allocated to foundational research and exploration - and not product milestones. Anyone who has spent time with me or Jakub knows we are the last people in the world who would push for the advancement of products over the advancement of research. We’re in the business of creating an automated scientist, and capabilities that were considered grand challenges just a few years ago (like IMO-level mathematical reasoning) now emerge as normal parts of the research process. We’re also seeing our models accelerate researchers worldwide, helping advance work across biology, mathematics, physics, and even our own research. Jakub and I put a lot of effort into ensuring that research stays focused on uncovering algorithms that will scale to the compute we’ll have a year from now. We protect mindshare and amplify discourse on exploratory work. We do this while recognizing that we’re also a deployment company - and that deployment gives us access to even larger-scale compute, richer feedback, and more room for exploration. Our researchers are passionate about having their work out in the world, and a special slice of our org is dedicated to making sure our deployments are delightful for end users. Our goal isn’t to turn research into a quarterly race. It’s to build a durable research engine - one that compounds learning over time and consistently turns long-horizon exploration into real, measurable advances, while ensuring those advances become valuable in the real world. That’s the roadmap we’re executing on. And while there have been ups and downs over the last decade (as you expect with any research program), I think most of our researchers would share my strong optimism today.
- 62Replies
- 81Reposts
- 1.2KLikes
- 442.3KViews
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@theojaffee ·
The best new detail in @kevinroose's book The AGI Chronicles was that soon after the release of GPT-2 in 2019, Dario Amodei (then OpenAI's VP of Research) wrote a still-unreleased memo called "The Path to AGI", describing how OpenAI could get to AGI within a few years. The plan was: 1. Train an LLM with all of the text on the internet and elsewhere 2. Expand the model's context window 3. Make it multimodal with images/video/etc 4. Use RL to improve its capabilities with math and coding 5. Use that to recursively self-improve And indeed, this is exactly what's happened! OpenAI trained GPT-3 in 2020 with all the text on the Internet, followed it up with GPT-4 in 2022, expanded its context with GPT-4-32k and GPT-4-Turbo in 2023, made it multimodal with GPT-4o in early 2024, figured out how to apply RL to the models with Q*, which became the o-series of models in late 2024, and is now starting to recursively self-improve with models like Astra and internal unreleased ones. The only thing he missed was that in order to raise the amount of capital needed to build AGI, on the order of billions of dollars (and separately, to better prepare society for increased capabilities through iterative development), labs would need to shift from small research nonprofits to large product-focused organizations, like big tech companies. The first killer product turned out to be a good chatbot with the capacity to answer questions better than Google, and the second killer product was an agent that can write code, use software on your computer, and do tasks for you and deliver you the result. But Anthropic eventually came around to both of these, following ChatGPT with Claude, beating OpenAI to the punch with Claude Code/Cowork, and now leading OpenAI on revenue. Some ideas are extremely powerful and can last a long time - even if they're incredibly simple, even if you think other people should know about them and markets should be pricing them in, even if you think your alpha cannot possibly last. If you had deeply internalized scaling laws and their implications in 2019, you could have made a trillion dollars. "Take a simple idea and take it seriously"
- 15Replies
- 16Reposts
- 241Likes
- 16.2KViews
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@EXM7777 ·
everyone's testing GPT-Astra on random 3D environments and sloppy games... so here's real feedback from actually working with it: - the most pleasant OpenAI model i've ever talked to, gives me Opus 4.5 vibes - exceptional at setting its own goals and prompting subagents through long horizon work - still behind Fable on frontend design - stupidly good at using the computer, this will open up insane workflows - much, much better at writing, looks like they finally cracked it - super efficient, doesn't hit limits that fast honestly and no i haven't tried it on games or 3d assets, i'm busy implementing it in my businesses sorry
- 32Replies
- 7Reposts
- 482Likes
- 40.6KViews
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@apoorv03 ·
OpenAI serving open-source models through Baseten is a big deal. They are embracing open source. Until now, OpenAI's pitch was "use our models." Now it is "commit your annual AI budget to us". We will get you the best model for each job, whether it's ours, someone else's, or open source. So if you're a large bank looking to commit $100M in AI spend, you centralize with OpenAI. One throat to choke. GPT handles the credit memos. Open-source handles the KYC volume at a fraction of the cost. A post-trained / specialized model handles fraud. This changes OpenAI's position. It stops competing only on whose model is best this season and starts competing to own the enterprise AI budget. A layer above being just an AI lab. Also proves AI is positive sum, not zero sum between closed and open source!
- 14Replies
- 28Reposts
- 350Likes
- 47.1KViews
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@mark_k ·
OpenAI just published a major new plan for its next phase: AGI should not be controlled by a tiny number of companies, governments or individuals. The goal is to make advanced AI abundant, affordable, safe and useful enough that everyone can benefit from it. The most important detail: @OpenAI says it is building an automated AI researcher, and internally believes that by March 2028, a significant fraction of its own research may be done by AI systems working alongside human researchers. The three big goals are clear: automate more of AI research, accelerate science and economic growth, and eventually give everyone on Earth a personal AGI. This is the real post-AGI question: not just who builds the most powerful model, but who gets access to that power.
- 41Replies
- 28Reposts
- 323Likes
- 14KViews
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@signulll ·
okay, enough openai criticism for a second (still gonna give product feedback when it’s deserved). it’s been a little too easy. i’ve been using gpt live a lot, like a lot lot, & it’s genuinely fucking amazing. the feature to let the model speak like a human “hmm”, “let me check”, brief pauses, & natural turn taking while calling a bigger model or searching or using tools in the background is a fantastic execution. almost all of my conversations have been nothing but pleasant, i can’t describe the feeling but it’s i think a little too good at this point. nothing else is particularly close in this category right now at all. anthropic’s voice experience still feels very very underbaked (after thought really), & gemini’s personality & intelligence seem lacking. & since it’s already reaching an enormous user base, every great gpt live interaction makes people more confident in ai as a product category instead of walking away thinking ai is a gimmick. really great work.
- 38Replies
- 15Reposts
- 495Likes
- 39.3KViews
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@ChrisGPT ·
OpenAI is hiring for a “Recursive Self Improvement Safety Researcher” About the role: “Preparedness is hiring strong technical executors to support preparations for recursive self-improvement. This work relies on reasoning about problems that might exist in the future, but might not exist now; so it’s especially important that people in this role are tasteful and strategic.” “The role is wide-ranging, covering any mitigation for loss of control risk, spanning the design and implementation of better pre-deployment risk-assessment, control measures, RSI-relevant training interventions, and turning one’s technical work into established institutional practices.” OpenAI’s original plan was to have an automated AI research intern that could perform meaningful ML research at roughly the level of a human research intern by September 2026 (DevDay happens to be September 29th) - remember, OpenAI has been showing off what is effectively guided, human directed RSI since GPT-5.3, all the way to GPT-5.6 Sol autonomously handling a Luna post-training run from a fairly under-specified prompt which I feel people don’t talk about a lot. - and with GPT-6, it fits perfectly that they’d need to hire for a role preparing for exactly this transition. I definitely don’t think it’s hype, OpenAI would not be paying up to $445K a year to prepare for recursive self-improvement if it viewed the problem as purely theoretical. OpenAI has clearly been cooking internally since June, they are well on their way toward the automated research intern milestone, and they are already heavily engaged in guided RSI with research debugging, training system optimization, experiment execution, post training, etc, etc.
- 17Replies
- 25Reposts
- 331Likes
- 34.5KViews
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@siliconvalleymm ·
Yesterday, I was at @OpenAI DevDay, where Sam Altman @sama introduced Dots - OpenAI’s new “always-on” AI agents. And I think it gives us a pretty good idea of where productivity is heading. Today, even when we use AI every day, we still have to initiate most things. We open ChatGPT, ask a question, explain what we need, review the answer, then come back with the next task. Dots change that dynamic. They learn how you work and keep working without waiting for the next prompt - drafting things for you, noticing what needs your attention, and proactively bringing things to you. And apparently, it only takes a couple of days for it to start learning the way you work. To me, that’s a much bigger change than simply getting better answers from AI. We’re getting closer to AI that understands how we work well enough to be useful before we even ask. Very curious to see what this does to our workdays over the next year. #DevDay
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@jaltma ·
This week's guest on Uncapped is @bradlightcap, COO at OpenAI. We talked about the history of OpenAI, the shift in AI from chat to agents, where new startups can endure, Codex, FDEs, working with Sam, and more. Hope you enjoy! (0:00) Intro (0:39) The early days of OpenAI (3:47) A research centric culture (7:32) Post-ChatGPT chapters (11:54) Sci-Fi future or good software (15:26) AI’s impact on rural communities (18:57) Codex and coding of the future (24:04) Doing a lot of things at once (27:55) What VCs should invest in (35:43) The software sell off (38:23) Using Codex over ChatGPT (42:32) FDEs and Private Equity (44:53) Working with Sam
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@goyalshaliniuk ·
OpenAI is making its enterprise platform more secure and easier to manage for teams building with AI. Private MCP servers now allow companies to keep their servers inside their own network while products like ChatGPT, Codex, and the Responses API connect through outbound-only HTTPS. This gives teams a safer way to connect internal tools and systems without exposing them publicly. OpenAI also introduced Workload Identity Federation and expanded Admin API controls. This means teams can manage access through cloud IAM workflows, reduce dependency on permanent API keys, set spend alerts, control model access, manage data retention, configure hosted tools, and track costs more granularly across features like file search and web search.
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@ChrisGPT ·
OpenAI had a sharp rise in revenue in Q4, and I think they could drive a similar derivative of growth this summer by advertising GPT 6 more aggressively to the public. Now, with OpenAI pushing toward the super app vision, where everything is unified in one place with more agentic features for mobile as well, the upside can be akin to the growth they saw in Oct-Dec for revenue. If they ship a true white collar agent that can operate on your desktop and handle real tasks, plus something closer to Anthropic’s Record a workflow feature on chrome - but also adding it to the desktop (where the model can observe the screen, interact with the desktop, and learn your personal workflows), that would pull in a lot more paying subscribers. A lot of white collar people such as myself would go from just using it at work to also using it heavily for personal projects at home.
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@alexeheath ·
I spent time with the Codex leadership team to understand OpenAI’s new super app strategy, and what it says about where AI is headed this year TLDR: AI coding is about to power a lot more than software engineering https://t.co/VYmkRXSco0
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@AIHighlight ·
🚨 OpenAI just published how their own engineers use Codex. Daily, across security, infra, frontend, and API teams. The five patterns that show up in the report (link at the end):
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@HedgieMarkets ·
🦔OpenAI plans to turn ChatGPT into a "superapp" with coding tools, AI agents, and paid integrations ahead of its IPO. The overhaul shifts resources toward enterprise clients, with 2 million businesses already at 40% of revenue and expected to hit 50% by year-end. ChatGPT has 900 million weekly users and 50 million paid subscribers. OpenAI projects a $14 billion loss this year and hasn't filed its S-1. My Take Altman said last year that apps would become obsolete because of AI, and now he wants to build a superapp. Coding tools are at the center of it. GitHub Copilot's coding tool billing fell apart last week when users burned through monthly credits in hours. 900 million people use ChatGPT every week, 50 million pay for it, and OpenAI still loses $14 billion a year. The superapp is supposed to change that. OpenAI has shifted hard toward enterprise, with 2 million businesses already at 40% of revenue and a target of 50% by December. But those are the same enterprise customers who discovered last week what AI tools cost and whether they produce anything. SpaceX goes public Thursday. OpenAI is redesigning its app. Hedgie🤗
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@Alfred_Lin ·
At @sequoia’s AI Ascent last week, @gdb told me something that stuck: in late 2024, AI wrote ~20% of @OpenAI's code. That number is now 80%. We also got into why human attention, not compute, is the real bottleneck in AI-augmented work, plus what it might mean to run an org of 100,000 agents. We’re grateful to Greg for joining us and many of the top founders/builders in AI. You can watch the full video here: 00:00 Intro 00:49 Compute Hunger Explained 02:13 Scaling Laws Mystery 03:31 New Architectures Ahead 04:42 How Close to AGI 06:46 Startup Playbook for AI 09:24 Inside OpenAI with Codex 11:11 Teams and Governance Shift 14:52 Security and Responsible Deployment 25:33 Science Frontiers and Wrap Up
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@BoringBiz_ ·
Chamath on OpenAI vs Anthropic's business model People consistently try to pit these two companies against each other. But it is important to remember that the core customer base for both companies are fundamentally different OpenAI remains the consumer first model of choice for AI, while Anthropic has become deeply embedded in the enterprise motion Just one of these have a large enough TAM to justify hundreds of billions in market capitalization if you are the leading LLM provider The revenue recognition also makes it apples to oranges. OpenAI reports their revenues on a net basis, excluding Microsoft licensing fees and partnership revenue share Anthropic talks about their revenue from a gross basis, similar to ARR or bookings for B2B software companies. API fees related to usage from AWS, for example, are not excluded in the Anthropic numbers that you hear about online Ultimately, two very different revenue and business models that often end up getting compared to each other because both are leading LLM providers. Worth a listen
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@HeyAbhishek ·
Best GitHub Repos to Build Real AI Skills in 2026: 1. OpenAI Cookbook https://t.co/Kzczt4Ovzm 2. OpenAI Agents SDK https://t.co/OLSaUPyNsM 3. OpenAI Evals https://t.co/yC2ICTvkVC 4. PydanticAI https://t.co/Dt8m4vjZMi 5. Hugging Face Agents Course https://t.co/ZIL1W2dUoL 6. AI for Beginners https://t.co/9Uu9IyX0my 7. Hugging Face 101 Course https://t.co/EwxC6EFWld 8. Hugging Face Smol Course https://t.co/C2h7aH7MlG 9. AI Engineer Handbook https://t.co/aeujyhE0Yk 10. AI Engineering Field Guide https://t.co/QkybsQMLpN Bookmark this.
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@RayFernando1337 ·
I’ve had early access to OpenAI dots and I’m not here to join in on the glaze fest. Is this a Grok Bot or Hermes killer right now? NO. But…Spaces and allowing agents to thrive in apps feel like the right direction for these agents. •• The Good What worked well during my usage was how proactive the agent is right from the start with my existing plugins/connectors. The team is shipping insanely fast and have been quickly making changes to fine tune this experience. •• Caveats With a slow internet connection in the middle of the South Pacific Ocean I didn’t have a good enough connection for voice and had to heavily rely on cloud. There were other quirks related to this that might be noise but worth mentioning. •• Bad at Writing I tried connecting my dot to GitHub and it performed a research task to make a Spotify wrapped + story about my weeks at sea where I made over 294 PRs and shipped over 30 releases. The design felt very GPT-4 like with lots of text and the story was even worse. •• Conclusion I need more time with this and need to get this hooked up on my Mac at home with Codex so my dot can fly. I think there is potential to have a dot like this have access to powerful tools, web, and apps like Space to write pages and interact with humans. I love the ease of use and speed of Grok Bot a lot. It is so fast to spin up many bots and have them quickly work together especially now as a team. I’ll also be doing a deep dive of Grok Bot vs dots and continue to share my honest insights as a solo engineer building software after leaving FANG. •• Disclosure & Philosophy I hate it when I read early access posts and they only share the good but leave out the quirks. As a fellow engineer who’s shipped to billions of people, I respect the engineers who build software and spend many hours away from their friends and family to build their life’s work. The views and opinions are from me. I have not been paid or compensated by OpenAI, in fact, I’m still a paying customer and haven’t received any compensated plans from OpenAI as of the writing of this post. As part of the Dev Early Access I plan to continue to gift my expertise, opinions, be it good or bad, with the intention that they also value their product enough to make improvements for the broader community.
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@VaibhavSisinty ·
OpenAI just shipped the biggest release in their history. I got in before launch. GPT-5.6 family : Sol, Terra, Luna. A new super app that merges @ChatGPTapp and Codex. Three modes: Chat, Work, Code. I sat down with @wulfie_bain_ from @OpenAI and we built a full startup on camera. Pitch deck, working web app, computer-use agents finding and fixing bugs live, and an autonomous 16-hour overnight run. The idea that changes everything: stop writing prompts. Start writing loops. Give the agent a goal and a way to check its own work. That's how a model runs overnight and comes back finished. We also answer the real questions what you build that OpenAI won't kill, tokens vs engineers, and why India wins in this era. Coder or not watch this one.
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@VaibhavSisinty ·
Let me explain what OpenAI just did with the new Codex update. Because most people are going to miss the actual story here. Codex is no longer a coding agent. It's a full operating layer. In one update they shipped: computer use across any app on your machine, a built-in browser with a commenting layer, native image generation through gpt-image-1.5, persistent memory across tasks, and 111 new plugins that combine skills, MCP servers, and app integrations. I've been saying for months that tools like Codex and Claude Code are becoming the default interface for everything. Not just code. Everything. Here's what most people are missing. The biggest unlock of the last year wasn't a new model. It was the shift to "do the work first by writing code, with tool calls, then talk about it." Research, ad ops, content pipelines, analytics, internal tools. All of it runs better through coding agents that call tools than through any chat interface. The people still using AI as a chat box are operating one stack behind. And now zoom out. OpenAI is reportedly shipping Spud in the next few weeks. A new base model that Greg Brockman has publicly called "two years of research" with a "big model feel." Sam Altman told employees it could "really accelerate the economy." If that lands as expected, it's at the same tier as Anthropic's upcoming Mythos. Now bolt that model onto the Codex super app I just described. Computer use. Browser. Image gen. Memory. Plugins. Skills. All running on a model designed for long-horizon agentic work. Here's the part nobody wants to say out loud. Anthropic could lose its edge of coding revenue very soon onc Spud hits Codex. And it has nothing to do with model quality. When a better coding model exists AND it's one click away from the ChatGPT tab people already pay $20-200 a month for, nobody runs evaluations. They switch that afternoon. The switching cost between Claude Code and Codex for most dev teams is approximately one API key change. No procurement. No 6-month contract cycle. You just swap. Distribution is the moat you can't build a defense against fast enough. The next era of work isn't about which AI you talk to. It's about which AI you let run your computer.
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@sabir_huss50540 ·
OpenAI is giving open-source developers up to $1,200 in ChatGPT Pro for free. Barely anyone is talking about it. If you maintain an open-source project, here's everything you can claim: → 6 months of ChatGPT Pro with Codex (up to $1,200 value) full Codex access for coding, triage, PR review, and maintainer workflows → API credits from OpenAI's $1M Codex Open Source Fund for projects using Codex in PR review, releases, and automation → Codex Security access deeper security scans for repos that need it (core maintainers) Who qualifies: Core maintainers or anyone running a widely-used public project. Doesn't perfectly fit the criteria but your project matters to the ecosystem? Apply anyway and tell them why. And you code in whatever you want. Codex, Cline, OpenCode, OpenClaw. The program still backs the work. Apply link in the reply 👇
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@CtrlAltDwayne ·
Feels like OpenAI changed something with the xhigh reasoning in Codex. It used to overthink and not be good for a lot of things, but xhigh seems to have had not only a tone shift in its thinking, but seems to think better and not overthink. Anyone else noticed?
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@EXM7777 ·
since the release of Sonnet 3.7, Anthropic was the ONLY company that seemed to understand how to leverage models horsepower... they mastered the art of building features designed for performance, not just "looking promising" but drastically transforming how we use AI daily but honestly OpenAI is getting closer... GPT Images 2.0 is a great example of that, same for Codex
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@rohanpaul_ai ·
OpenAI’s new target is an autonomous AI researcher that can break big problems into parts, run many agents in parallel, test ideas, read results, fix mistakes, and keep going for days with little help. ~ MIT Technology Review The bet is that coding agents are the first real proof this can work, because software engineering already looks like research in miniature: long chains of decisions, failed attempts, tool use, memory, and step-by-step checking. So the jump from Codex to a research system is not about one smarter answer, but about building a machine that can plan, delegate, verify, and recover across a full project instead of a single prompt. --- technologyreview .com/2026/03/20/1134438/openai-is-throwing-everything-into-building-a-fully-automated-researcher/
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@shushant_l ·
I'm amazed most people still use AI just to generate code snippets. Here's how OpenAI Codex can build, debug, test, review, and automate entire coding projects with AI. --- 1. OpenAI Codex is an AI coding and software engineering agent, not just a code generator. --- 2. It understands projects before writing or modifying code. --- 3. It can write new code, edit existing code, and refactor projects. --- 4. Codex can run tests when supported and validate changes. --- 5. It reviews code and delivers review ready updates. --- 6. It helps debug errors and fix software issues faster. --- 7. You can use it inside ChatGPT, the Codex App, and Codex CLI. --- 8. It also works with VS Code, JetBrains IDEs, GitHub, and mobile apps. --- 9. Enterprise environments can integrate Codex into development workflows. --- 10. It supports coding, engineering, and productivity tasks. --- 11. It can build APIs, optimize performance, and convert programming languages. --- 12. Codex helps review pull requests and modernize legacy code. --- 13. It creates documentation, reports, spreadsheets, and specifications. --- 14. It supports popular languages like Python, JavaScript, Java, C++, Go, Rust, SQL, HTML, CSS, and more. --- 15. The ideal workflow is analyze, plan, code, test, fix, review, and deploy. --- 16. The best prompts include your goal, project context, requirements, constraints, and expected output. --- 17. It can read source code, documentation, configuration files, logs, and repositories. --- 18. It generates code, tests, SQL queries, deployment scripts, documentation, and technical reports. --- 19. Clear instructions, project context, and version control produce the best results. --- 20. Human review and security checks are still essential before production deployment. --- To learn more, check the infographic. ---
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@milesdeutscher ·
GPT-5.6 first impressions (raw thought dump): • Better than Fable? From my tests so far, Fable still seems like the king of raw intelligence and true autonomous work. However, GPT-5.6 models are hands down the better "daily drivers" due to their pricing/speed. I'll be using a combo of both to maximize intelligence and cost efficiency. • Thoughts on GPT Work This is obviously OpenAI's attempt to create a competitor to Cowork. No doubt GPT Work is powerful, but for me, I like how I have Cowork set up, and I'll just stick with it for now, since all my workflows are there and I like the other features (Dispatch, Plugins, my Claude Skills, etc.). • My favorite model from the bunch GPT-5.6 Sol. It's insanely autonomous and seems to just push through to get work done - no matter the blockers. • Frontend improvements Massive improvement across the board in frontend/design work - it seems like over the past month, most AI labs have made great improvements here. I think the typical AI slop frontend days are behind us. • Thoughts on hosted sites My team and I will be using the new hosted sites to share ideas. Definitely a cool way to quickly build and share ideas, and I think if you're a small team/department, you'll really like this feature. Overall - only a few hours since release, but this is generally what I expected. Going to plan some content for you guys now. If you have any requests or want to see anything in particular, leave it in the comments below - I'll read everything. ChatGPT Work Ultimate Guide? GPT-5.6 trading bots? Let me know!
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@sentient_agency ·
this is the most underrated AI resource on the internet. OpenAI quietly maintains a free repository of working code recipes for every single thing you can build with their models, and almost nobody talks about it. It's called the OpenAI Cookbook. Hundreds of recipes. Updated weekly. Open source. Here's what's actually inside: → Full RAG pipelines with vector databases wired end to end → Function calling and tool use patterns for real agents → Fine-tuning workflows with dataset prep and evaluation → Vision recipes for OCR, document parsing, and image analysis → Realtime API patterns for voice agents and live streaming → Embedding strategies for semantic search at scale → Structured output recipes that never break your JSON schema → Batch processing for cutting API costs by 50% → Audio recipes for transcription, translation, and TTS Every recipe is a runnable Jupyter notebook. Every line of code works. Every pattern is production-tested. https://t.co/A0x1rbnVMb https://t.co/OCbaYm17WX The same engineers building OpenAI's API write these recipes. You're getting the internal patterns for free.
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@eric_seufert ·
An alternate information space seems to have developed around the chatbot advertising market that has led people to believe that OpenAI's advertising product is an abject failure, ads in chatbots are fundamentally nonviable anyway, and the entire category will never generate a meaningful amount of revenue. Meanwhile, in the primary information space, we know that Alphabet saw Search queries reach an all-time high in Q1 while Search revenue grew by 19% and ads in AI Overviews monetize at parity with those in traditional Search. The company is expanding advertising in its AI-generated and chatbot surfaces cautiously but deliberately: it introduced formats like Conversational Discovery ads and Highlighted Answers in AI Mode in May of this year. The latest usage numbers shared by Google are 2.5BN MAU for AI Overviews and 1BN MAU for AI Mode. AI Overviews is roughly two years old; AI Mode is roughly one year old. Further, we know that OpenAI reached $100MM in annualized advertising revenue a mere six weeks after introducing advertising with what was essentially a manually operated product. Since then, OpenAI has executed at breakneck speed to introduce new features: CAPI and pixel integrations in May and Custom Audiences last month. ChatGPT now serves more than 1BN MAU and was already serving more than 900MM WAU in March. ChatGPT launched at the end of 2022. OpenAI's advertising revenue goal for 2030 is incredibly ambitious; I have no affiliation with OpenAI, and I don't have any quantitative indication of whether they'll meet that goal. But to conclude that OpenAI's advertising efforts are somehow failing now, or that the market won't evolve into something meaningful, feels to me totally detached from the primary information space.
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@theinformation ·
Sam Atlman’s renaming of the Product org to “AGI Deployment” makes the strategy explicit—OpenAI isn’t just building models, it’s racing to operationalize them at scale. https://t.co/oiKCX7YWGc
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@DivyanshT91162 ·
Wait... OpenAI is literally paying developers to build with its API. 🤯 Most people are out here searching for API free trials. Meanwhile, OpenAI quietly has a program that can give eligible users access to up to $50,000 worth of API credits just by flipping a setting. Here's the trick: → OpenAI Dashboard → Data Controls → Sharing → Opt into the Data Sharing Program The tradeoff? Your API data can be used by OpenAI to improve future models. That's why it's perfect for: • AI experiments • Personal projects • Agent workflows • MVPs and prototypes • Learning the API And not ideal for: • Client projects • Private company data • Confidential codebases If you're spending money testing AI ideas before checking this setting, you might be doing it the hard way. One toggle. Potentially thousands of dollars in credits. Most developers never notice it.
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@Layton_Gott ·
OpenAI DevDay is Tuesday... and the rumor list is getting kind of ridiculous... > Aeon Probably the biggest one. An always-on / persistent OpenAI agent that can keep working after you leave, remember what it's doing, react to events and come back when it needs you. Peter Steinberger joined OpenAI in February specifically to work on bringing agents to everyone... and considering he literally built OpenClaw, I'd be shocked if he wasn't heavily involved in whatever Aeon turns into. > Jalapeño OpenAI's first custom AI chip is already real. But deployment starts later this year, Gen 2 is already deep in development, and Gen 3 is being designed. So DevDay could give us: > rollout details > what models will actually run on it > API speed / price implications > ultra-fast inference > possibly a look at Gen 2 OpenAI claims Jalapeño can cut end-to-end latency by up to 3.6x vs Nvidia GB300 in some workloads. > Managed Agents Unreleased OpenAI code has shown: - agents - environments - sessions - skills - plugins - hosted + potentially self-hosted environments basically somewhere to actually BUILD + deploy long-running agents. > Astra 6.1 Probably the most obvious model wildcard. There are also rumors OpenAI has another model already in training as an answer to the latest Opus jump. > Bel preview Way more speculative... but people have been waiting for this one forever and there have been reports OpenAI could pull future work forward if competition forces their hand. > new video model / API The old Sora video API just disappeared with no replacement listed... 5 days before DevDay. That timing is VERY interesting. > deeper Skills + Plugins This one almost feels guaranteed to matter somehow. Give an agent: - skills - tools - external apps - its own environment - persistent state and suddenly OpenAI has an actual agent platform instead of a bunch of separate features. > new open models possible. OpenAI has talked about doing more here, but I haven't seen enough to expect it Tuesday. > hardware / AI device tease people keep predicting it. I still think DevDay is WAY more likely to be about developers + agents than a full hardware reveal. My actual prediction: Aeon + Managed Agents are the big story. Anything else is a bonus. OpenAI already shipped Astra, Sol and Luna BEFORE its biggest developer event of the year... there has to be a reason lol.
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@TimHaldorsson ·
Next phase of AI will be focused on adoption and education the team at @OpenAI have their "OpenAI Academy" with a lot of good educational content and courses focused on how to use their products and a large focus on Codex that is getting better and better. you can really see that their team is laser focused on coding and enterprise market.
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@ActivateSignal ·
Pragya Misra, who leads Strategy and Global Affairs for OpenAI in India and was the company's first hire in the country, sits down with Aakrit Vaish at Mumbai Tech Week to unpack OpenAI's India bet and why Codex is changing who gets to build. Two years in, Pragya has had a front-row seat to OpenAI's growth from startup mode to a generational platform, and makes the case that for OpenAI to win globally, it has to win in India. The conversation goes deep on Codex's explosive adoption, the shift from coding model to product-development tool, and what OpenAI is actually building on the ground in India. In this conversation, they go deep on: 0:00 Day one of Mumbai Tech Week and what Pragya does at OpenAI 2:02 Why she came to MTW and what she saw this year 3:38 Founder stories: a Marathi voice-first wealth manager for Tier 2/3 5:29 Why Codex is suddenly ramping up 6:22 Codex grew 27x in 2026, India a top-five country 7:06 How non-coders are becoming builders 7:56 The Surya Namaskar app demo for PM Modi 9:17 Codex is a product-development model, not just coding 9:48 How Codex changed her day-to-day ("build me a chief of staff") 11:12 OpenAI's India plans: the team and the people joining 14:18 20+ people on the ground and offices in Delhi, Mumbai, Bangalore 14:56 Parting advice for builders: what's coming at OpenAI 15:13 Compressing model releases: every 6-7 weeks, 80-85% coded by Codex 17:04 GPT-5.5, the financial sector, and the AWS partnership. If you're a founder, builder or operator thinking about AI, or what OpenAI is doing in India, this one's for you. @aakrit @pragyamisra @OpenAI
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@DanielMiessler ·
So one thing I find really interesting right now is the difference in how it seems OpenAI and Anthropic are approaching the market. I see Anthropic as primarily, this is an oversimplification, but I think Anthropic is primarily going after the enterprise and trying to be all the internal workflows, all the unified entity context. Kind of almost like a Palantir-type situation of understanding the business and helping the business actually perform operationally. I feel like that is the massive force of what they're pushing into. Obviously, they care about consumer as well, so both are doing both. But I feel like OpenAI is largely focusing on the bigger consumer vision. So, I opened ChatGPT the other day, and I noticed it said, "Hey, connect your Apple Health." So I feel like OpenAI is going after all the personal stuff, and they hired Jony Ive just as another piece of sort of evidence here. They hired Jony Ive to build a piece of hardware, which I think, and kind of the interpretation is basically that's meant to replace the iPhone or the phone in general. Like, to have an AI device where you're talking to your AI. And Sam has been talking about this massively. Dario seems to talk more about the future and work and the enterprise. Again, with some exceptions. And Sam seems to talk more about the future of like the personal ecosystem, having an agent always available. And obviously, given what I talk about and my sort of orientation, I really find that OpenAI thing quite compelling. Obviously, I like both. I'm really interested in both. But I feel like OpenAI is more aligned in this particular case with the way I've seen AI going, which is you have your personal assistant, that is who you're talking to. The personal assistant is then doing all the different things for you in all these different places, and basically operating on your behalf. And with the merging of ChatGPT and Codex into the single app, and the more and more connectors that OpenAI is bringing in, it really does feel like they are definitely heading in this consumer sort of orientation. And what I find interesting about this is both are multi-trillion dollar businesses, right? Replacing the operations layer of all these businesses is just extremely lucrative, right? And that's where Anthropic is sort of heading. But also just becoming the single agent, becoming Her or becoming Jarvis for all of humans, right, is kind of the TAM for OpenAI here, in this particular lens. And that is also a multi-trillion dollar market, right? So I feel like both are doing something extraordinary, really huge, that the other is kind of not touching as much, and I just find that really interesting. Curious to hear if you see it differently, or if you kind of agree with this approach.
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@WesRoth ·
OpenAI’s revenue reportedly accelerated again in July. CFO Sarah Friar told employees that the company’s annualized recurring revenue exceeded the level recorded during the entire second quarter. The exact number was not disclosed. But OpenAI says three products are driving the momentum. GPT-5.6, ChatGPT Work and Codex. Board chair Bret Taylor also admitted that OpenAI entered the year playing catch-up to Anthropic in AI coding. Now, he says, Codex is gaining traction. And some Claude Code users are reportedly looking for alternatives after receiving unexpectedly high bills. That may become one of the most important battles in AI. Not just which model writes better code but which one delivers enough value to justify the cost of using it every day. OpenAI already has massive consumer distribution through ChatGPT. Now it is turning that audience into a platform for coding, agents and professional work.
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@CodeByNZ ·
“Elon Musk: I am the reason OpenAI exists. It wouldn’t exist without me.” What makes that statement so interesting is how different OpenAI looks from the vision he originally described. The idea was supposed to be: > open-source > nonprofit > and the opposite of Google’s closed AI dominance. That’s literally where the name “OpenAI” came from. Now the company is raising tens of billions, building closed frontier models, partnering heavily with Microsoft, and becoming one of the most powerful centralized AI companies on earth. The AI race moved so fast that even the labs created to prevent concentrated AI power slowly became concentrated AI power themselves.
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@Suryanshti777 ·
OpenAI's own coding agent just got forked into a weapon against OpenAI's own business model. Open Interpreter took Codex, ripped out the parts that only work well with GPT, and rebuilt it to squeeze max performance out of open models instead — Kimi K3, DeepSeek, Qwen, GLM. Type `/harness` and switch the entire agent behavior mid-session. Same interface, different brain underneath. It also speaks the actual Codex exec protocol. One line swap in your existing SDK code and you're running open models through the same pipeline you built for Codex. 66.8k stars. Apache-2.0. Fork it, ship it, no permission needed. The lesson here isn't "open models are catching up." It's that the harness — the thing wrapped around the model — might matter more than the model itself. And that part just got forked wide open. Link in comments 👇
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@frantzfries ·
Feel like we're not talking enough about how OpenAI is the only platform I can think of that *doesn't* have a "chat with your docs" feature
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@boyuan_chen ·
Distribution. OpenAI just shipped a Codex plugin that runs inside Claude Code. You can trigger Codex for code review, adversarial review, or task delegation without leaving Anthropic's CLI. Piggybacks on your existing ChatGPT subscription. Think about what this means. OpenAI gets usage data, billing, and developer habit from inside a competitor's tool. Every Codex call from Claude Code feeds OpenAI's flywheel while the developer thinks they're using Anthropic. Model benchmarks decide who's best this quarter. Workflow integration decides who's default next year. https://t.co/X6LfmGg6Ss
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