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GPT‑5.6 took on a complex coding task spanning nearly 1,000 lines for Ankit Aich at Snorkel AI, handling the work from start to finish without repeated prompting or hand-holding.

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OpenAI Developers

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63,448 просмотров • 2 месяцев назад •via X (Twitter)

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📣 OpenAI Developers's GPT-5.6 family is now generally available and rolling out in GitHub Copilot. GPT-5.6 comes in three variants: ☀️ GPT-5.6 Sol: the highest reasoning ceiling in the family. Best for complex reasoning over large codebases and demanding, long-running agentic work. 🌎 GPT-5.6 Terra: the balanced default. A strong all-round choice for everyday interactive and agentic coding. 🌕 GPT-5.6 Luna: a lightweight, cost-efficient variant for smaller, faster tasks and the lowest-cost option in the family. Try one, two, or them all in Visual Studio Code or the GitHub Copilot app.
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📣 OpenAI Developers's GPT-5.6 family is now generally available and rolling out in GitHub Copilot. GPT-5.6 comes in three variants: ☀️ GPT-5.6 Sol: the highest reasoning ceiling in the family. Best for complex reasoning over large codebases and demanding, long-running agentic work. 🌎 GPT-5.6 Terra: the balanced default. A strong all-round choice for everyday interactive and agentic coding. 🌕 GPT-5.6 Luna: a lightweight, cost-efficient variant for smaller, faster tasks and the lowest-cost option in the family. Try one, two, or them all in Visual Studio Code or the GitHub Copilot app.

GitHub

483,421 просмотров • 2 месяцев назад

⚡️OPENAI: GPT-5.6 SOL IS 54% MORE TOKEN EFFICIENT ON AGENTIC CODING Sam Altman says the new GPT-5.6 Sol model is 54% more token efficient on agentic coding tasks and is “as good or better” than competing models. He also said OpenAI aims to offer better value with “every enterprise now is thinking about spend and the value they’re getting in exchange for AI.”
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⚡️OPENAI: GPT-5.6 SOL IS 54% MORE TOKEN EFFICIENT ON AGENTIC CODING Sam Altman says the new GPT-5.6 Sol model is 54% more token efficient on agentic coding tasks and is “as good or better” than competing models. He also said OpenAI aims to offer better value with “every enterprise now is thinking about spend and the value they’re getting in exchange for AI.”

Coin Bureau

33,775 просмотров • 2 месяцев назад

Deployment is only the first step. You need to learn from it and take the next steps toward positive ROI. Our journey: Step 1. Nine month ago, our robot already assembled a complex real product for our customer, from start to finish. But it wasn’t reliable. Step 2. Our robot performed a complex benchmark task reliably 100 times in a row. Step 3. (Completing) Our robot assembling a complex real product from start to finish 100+ times in a row.
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Deployment is only the first step. You need to learn from it and take the next steps toward positive ROI. Our journey: Step 1. Nine month ago, our robot already assembled a complex real product for our customer, from start to finish. But it wasn’t reliable. Step 2. Our robot performed a complex benchmark task reliably 100 times in a row. Step 3. (Completing) Our robot assembling a complex real product from start to finish 100+ times in a row.

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11,428 просмотров • 1 месяц назад

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❝ We make the world, as workers. No lights go on, none of the wheels on the railways turn, nothing gets made, without a worker putting their hand or brain to that task… We make the world work but it doesn’t work for our people. Mick Lynch dropping truth bombs at #TressellFest

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Anthropic’s Boris Cherny argues that for certain modern, model-friendly codebases, especially TypeScript/React projects, coding is already effectively "solved". AI agents can write nearly 100% of the code, while humans shift from hand-coding to directing, reviewing, integrating, and scaling many small PRs at once.
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Anthropic’s Boris Cherny argues that for certain modern, model-friendly codebases, especially TypeScript/React projects, coding is already effectively "solved". AI agents can write nearly 100% of the code, while humans shift from hand-coding to directing, reviewing, integrating, and scaling many small PRs at once.

Chubby♨️

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Opus 5 is a modest upgrade, but it has tough competition in GPT-5.6 Sol Initial thoughts from our early access: > Clearer writing than Fable's dense communication style > Still gets too verbose compared to GPT models (even when explicitly instructed to keep text purposeful) > Strong on browser use inside Hyperagent > Strong on reviewing complex data to justify a decision > Less likely to take creative risks. More of a "rule follower" personality. Costs shown for five test cases (GPT-5.6 Sol was consistently much cheaper) Explore and judge the actual work for yourself in thread 👇
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Opus 5 is a modest upgrade, but it has tough competition in GPT-5.6 Sol Initial thoughts from our early access: > Clearer writing than Fable's dense communication style > Still gets too verbose compared to GPT models (even when explicitly instructed to keep text purposeful) > Strong on browser use inside Hyperagent > Strong on reviewing complex data to justify a decision > Less likely to take creative risks. More of a "rule follower" personality. Costs shown for five test cases (GPT-5.6 Sol was consistently much cheaper) Explore and judge the actual work for yourself in thread 👇

Hyperagent

154,334 просмотров • 1 месяц назад

Termius + Tailscale + tmux You don't need your laptop to build with Claude Code, Codex, or any other AI coding agent: → Run your agent inside tmux to keep the session alive → Use Tailscale for secure access to your laptop → Connect with Termius over SSH from iPhone, iPad, or Android Start coding at your desk. Continue on the go.
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Termius + Tailscale + tmux You don't need your laptop to build with Claude Code, Codex, or any other AI coding agent: → Run your agent inside tmux to keep the session alive → Use Tailscale for secure access to your laptop → Connect with Termius over SSH from iPhone, iPad, or Android Start coding at your desk. Continue on the go.

Termius

1,213,527 просмотров • 1 месяц назад

🤖 Introducing Taskade’s Autonomous Task Agents, now in Beta! Imagine an AI Agent creating tasks, adjusting timelines, and auto-prioritizing—managing your project from start to finish. Reschedule, assign, and keep workflows on track—all at once! For beta, reply ‘AI Project`
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🤖 Introducing Taskade’s Autonomous Task Agents, now in Beta! Imagine an AI Agent creating tasks, adjusting timelines, and auto-prioritizing—managing your project from start to finish. Reschedule, assign, and keep workflows on track—all at once! For beta, reply ‘AI Project`

Taskade

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Since launching in February, the Google AI Professional Certificate has become the most popular generative AI certificate of all time on Coursera. 🥳 Now, we're expanding the course to include lessons on vibe coding. With vibe coding, anyone can learn how to use everyday language to describe what they want to produce, letting AI handle the technical coding to build robust apps, automate workflows, or create custom dashboards — no prior coding experience needed. Whether you’re streamlining logistics at a multinational company or designing a custom app for your team, the Google AI Professional Certificate is built to help you learn how to make AI work for you. Learn more →
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Since launching in February, the Google AI Professional Certificate has become the most popular generative AI certificate of all time on Coursera. 🥳 Now, we're expanding the course to include lessons on vibe coding. With vibe coding, anyone can learn how to use everyday language to describe what they want to produce, letting AI handle the technical coding to build robust apps, automate workflows, or create custom dashboards — no prior coding experience needed. Whether you’re streamlining logistics at a multinational company or designing a custom app for your team, the Google AI Professional Certificate is built to help you learn how to make AI work for you. Learn more →

News from Google

44,591 просмотров • 1 месяц назад

As AI agents get better at computer and tool use, or writing code on the fly for a task, we're going to be able to solve much broader domains of knowledge work. Here's an example of Box AI with the new Claude Skills to generate a clean powerpoint file from existing data.
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As AI agents get better at computer and tool use, or writing code on the fly for a task, we're going to be able to solve much broader domains of knowledge work. Here's an example of Box AI with the new Claude Skills to generate a clean powerpoint file from existing data.

Aaron Levie

30,287 просмотров • 11 месяцев назад

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua
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GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

Chris

250,919 просмотров • 2 месяцев назад

Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning
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Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Alex Ratner

495,851 просмотров • 1 год назад

I Made a Game for my daughter based off of her favorite Teddy Bear and her as the main character. Took me about 30 minutes using Codex from OpenAI along with GPT Images 2.0. The Music is AI, the graphics AI and the entire code is AI. I have 0 coding experience other than HTML. The character looks just like her and the teddy bear is nearly exact to the picture I provided.
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I Made a Game for my daughter based off of her favorite Teddy Bear and her as the main character. Took me about 30 minutes using Codex from OpenAI along with GPT Images 2.0. The Music is AI, the graphics AI and the entire code is AI. I have 0 coding experience other than HTML. The character looks just like her and the teddy bear is nearly exact to the picture I provided.

Brian Krassenstein

44,130 просмотров • 4 месяцев назад

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.
2:30

Traditional coding benchmarks do not reflect how software is actually built and maintained. That's why we built a new benchmark, APEX-SWE, in partnership with Cognition. It measures whether AI models can perform complex, real-world software engineering work to ship systems that work and debug them when they don't. OpenAI GPT 5.3 Codex (High) tops the leaderboard at 41.5% on Pass@1.

adarsh

218,501 просмотров • 5 месяцев назад

holy sh*t, it can't be f**king real you can use GPT-5.6 and claude for FREEEEE just put GPT-5.6 Luna and Claude Haiku 4.5 behind a free chat with zero sign-up required what you get for free: - GPT-5.6 Luna - Claude Haiku 4.5 - GPT-5.4 mini - Mistral Small 4 - gpt-oss 120B - Gemma 4 31B voice chat and image generation also available private by design how to use it (under 10 seconds): step 1: go to (no sign-up, no redirect) step 2: pick your model from the dropdown GPT-5.6 Luna, Claude Haiku 4.5, or any of the others step 3: start typing that's it. you're chatting with a frontier model for $0 important: > anonymous means rate limits are tighter than paid tiers > if you hit a limit, switch models or come back later > your conversations stay private
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holy sh*t, it can't be f**king real you can use GPT-5.6 and claude for FREEEEE just put GPT-5.6 Luna and Claude Haiku 4.5 behind a free chat with zero sign-up required what you get for free: - GPT-5.6 Luna - Claude Haiku 4.5 - GPT-5.4 mini - Mistral Small 4 - gpt-oss 120B - Gemma 4 31B voice chat and image generation also available private by design how to use it (under 10 seconds): step 1: go to (no sign-up, no redirect) step 2: pick your model from the dropdown GPT-5.6 Luna, Claude Haiku 4.5, or any of the others step 3: start typing that's it. you're chatting with a frontier model for $0 important: > anonymous means rate limits are tighter than paid tiers > if you hit a limit, switch models or come back later > your conversations stay private

Sakata

244,105 просмотров • 19 дней назад

Been giving my M3 Ultra Studio some love and tinkering with MLX. Started the afternoon at 42 tok/s. Now: 73 tok/s, and there’s still more on the table. Running Qwen3.8-Flash-Next @ 4-bit locally. Now for the crazy bit: Artificial Analysis Intelligence Index v4.3: GPT-5.6 Sol 39 (Medium) GPT-5.6 Sol 42 (High) Qwen3.8-Flash-Next 42 GPT-5.6 Sol 44 (XHigh) GPT-5.6 Sol 47 (Max) Grok 4.5 39 (High) Claude Sonnet 5 38 (Max) *Qwen score currently estimated by Artificial Analysis. A 4-bit open-weights model running at 73 tok/s on a Mac under my desk... with an estimated intelligence score matching GPT-5.6 Sol at High reasoning. Local AI is getting ridiculous. 🔥 Plus, this model does not say no to a bit of red teaming. Once I am happy with it, I will do a pr for Jun Kim's oMLX :)
0:48

Been giving my M3 Ultra Studio some love and tinkering with MLX. Started the afternoon at 42 tok/s. Now: 73 tok/s, and there’s still more on the table. Running Qwen3.8-Flash-Next @ 4-bit locally. Now for the crazy bit: Artificial Analysis Intelligence Index v4.3: GPT-5.6 Sol 39 (Medium) GPT-5.6 Sol 42 (High) Qwen3.8-Flash-Next 42 GPT-5.6 Sol 44 (XHigh) GPT-5.6 Sol 47 (Max) Grok 4.5 39 (High) Claude Sonnet 5 38 (Max) *Qwen score currently estimated by Artificial Analysis. A 4-bit open-weights model running at 73 tok/s on a Mac under my desk... with an estimated intelligence score matching GPT-5.6 Sol at High reasoning. Local AI is getting ridiculous. 🔥 Plus, this model does not say no to a bit of red teaming. Once I am happy with it, I will do a pr for Jun Kim's oMLX :)

Ash Hart

18,827 просмотров • 5 дней назад

We just created a music video for Timbaland! This was SO FUN to work on. This took us less than TWO WEEKS from start to finish. Tim and his team were amazing to work with and we’re excited to do more of these with him!
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We just created a music video for Timbaland! This was SO FUN to work on. This took us less than TWO WEEKS from start to finish. Tim and his team were amazing to work with and we’re excited to do more of these with him!

PJ Ace

419,990 просмотров • 1 год назад

Free GPT-5.6 Sol/Terra/Luna + Claude Sonnet 4.6 + a full IDE one code editor just dropped a 14-day Pro trial with popular AI models ( no card required, no BS ) what you get: - GPT-5.6 Sol, Terra, Luna (full family) - Claude Sonnet 4.6 - two weeks of real coding - native IDE built in Rust (10x faster than VS Code) - multiplayer coding + native AI integration how to get: 1. go to zed(.)dev, sign in via GitHub (account must be 1+ month old) 2. click Pricing, select Pro, hit "Start Trial Now" 3. download their IDE 4. open a project -> click the star icon bottom-left -> pick your model in other words, you get a full-featured IDE with leading AI tools that work for free for 14 days - all for $0. based on the tests I've run, I'd say the speed and ease of use are really great - on par with Cursor. btw I recently wrote a full guide on ways to pay $0 for every subscription you have - this is just one of them worth to read:
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Free GPT-5.6 Sol/Terra/Luna + Claude Sonnet 4.6 + a full IDE one code editor just dropped a 14-day Pro trial with popular AI models ( no card required, no BS ) what you get: - GPT-5.6 Sol, Terra, Luna (full family) - Claude Sonnet 4.6 - two weeks of real coding - native IDE built in Rust (10x faster than VS Code) - multiplayer coding + native AI integration how to get: 1. go to zed(.)dev, sign in via GitHub (account must be 1+ month old) 2. click Pricing, select Pro, hit "Start Trial Now" 3. download their IDE 4. open a project -> click the star icon bottom-left -> pick your model in other words, you get a full-featured IDE with leading AI tools that work for free for 14 days - all for $0. based on the tests I've run, I'd say the speed and ease of use are really great - on par with Cursor. btw I recently wrote a full guide on ways to pay $0 for every subscription you have - this is just one of them worth to read:

kaize

35,775 просмотров • 1 месяц назад