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Introducing Cognee v1.0: a major breakthrough in agentic intelligence. It is 145% better than Opus 4.8 and GPT 5.5 at long context memory retrieval. Cognee allows a 100 BILLION token context window 100,000x more than Claude. It's: - 6.9x cheaper than GPT 5.5 and Opus 4.8 - Cold starts... show more

Vasilije

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846,259 次观看 • 1 个月前 •via X (Twitter)

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Grok 4.5 is a "Wow!" moment. 🤯 > Opus 4.8 & GPT-5.5 level on benchmarks > Costs less than Sonnet 5 Is that real? Hard to believe. Added Grok 4.5 as an implementer agent in fable-advisor. It replaces Sonnet 5. Use Fable as the orchestrator and a combo of: 1. Grok 4.5 2. Opus 4.8 3. GPT-5.5 As implementer agents. Uses the Grok Build CLI so if you log in with an X premium account you don't have to pay API fees.

Grok 4.5 is a "Wow!" moment. 🤯 > Opus 4.8 & GPT-5.5 level on benchmarks > Costs less than Sonnet 5 Is that real? Hard to believe. Added Grok 4.5 as an implementer agent in fable-advisor. It replaces Sonnet 5. Use Fable as the orchestrator and a combo of: 1. Grok 4.5 2. Opus 4.8 3. GPT-5.5 As implementer agents. Uses the Grok Build CLI so if you log in with an X premium account you don't have to pay API fees.

Dan McAteer

191,013 次观看 • 1 个月前

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

Prime Intellect engineer: "everyone's bragging about a million-token context. here's what they don't tell you. at 256k tokens GPT-5.5 scores 80% on retrieval. push it to a million and it drops to 36%. the model accepts the context, it just can't reason across it. people call it context rot." in a 20-minute talk he explains why bigger context windows won't save your agents. continual learning + training on your own traces + real environments - that's the fix. Watch the talk, then save!

Carnage

745,910 次观看 • 1 个月前

Introducing GPT-5.5 A new class of intelligence for real work and powering agents, built to understand complex goals, use tools, check its work, and carry more tasks through to completion. It marks a new way of getting computer work done. Now available in ChatGPT and Codex.

Introducing GPT-5.5 A new class of intelligence for real work and powering agents, built to understand complex goals, use tools, check its work, and carry more tasks through to completion. It marks a new way of getting computer work done. Now available in ChatGPT and Codex.

OpenAI

13,194,743 次观看 • 3 个月前

$Microsoft just dropped a 17-page paper - "Less Context, Better Agents" - proving the thing nobody building agents wants to admit: Your agent isn't failing because it needs more context. It's drowning in the context it already has. They ran GPT-5 four ways on one 50-task benchmark (via MCP): Full history → bloated, pricey, more errors Trim to the last 5 tool calls → 79% done Add light summarization → 91.6%, on a fraction of the tokens Less context. Fewer tokens. Higher completion. Everyone's racing to cram MORE into the window. Microsoft just published the receipt that the opposite wins for long-running agents. Rewired how I build agents this week. Paper ↓$

Microsoft just dropped a 17-page paper - "Less Context, Better Agents" - proving the thing nobody building agents wants to admit: Your agent isn't failing because it needs more context. It's drowning in the context it already has. They ran GPT-5 four ways on one 50-task benchmark (via MCP): Full history → bloated, pricey, more errors Trim to the last 5 tool calls → 79% done Add light summarization → 91.6%, on a fraction of the tokens Less context. Fewer tokens. Higher completion. Everyone's racing to cram MORE into the window. Microsoft just published the receipt that the opposite wins for long-running agents. Rewired how I build agents this week. Paper ↓

Archive

18,242 次观看 • 1 个月前

There is no better place on the internet to learn about Context Engineering than this repo. It's literally a course with a learning path. It gathers the best resources and covers the theory + code for anything related to context, RAG, memory, and agentic systems and more.

There is no better place on the internet to learn about Context Engineering than this repo. It's literally a course with a learning path. It gathers the best resources and covers the theory + code for anything related to context, RAG, memory, and agentic systems and more.

ℏεsam

48,999 次观看 • 1 年前

$Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.$

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,208,877 次观看 • 3 个月前

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Daniel San

51,706 次观看 • 3 个月前

Rockstar spent 7 years, 2,000+ developers, and $500 million to build Red Dead Redemption 2. Kimi K3 rebuilt the same scene in 10 hours. One prompt. 4 million tokens. It's open source. 2.8 trillion parameters. A 1 million token context window. It beats Claude Opus 4.8 and GPT-5.6. It sits on par with Fable 5. Fable 5 results, at Sonnet pricing. The wall between you and a AAA game used to be a studio. Now it's a prompt.

Rockstar spent 7 years, 2,000+ developers, and $500 million to build Red Dead Redemption 2. Kimi K3 rebuilt the same scene in 10 hours. One prompt. 4 million tokens. It's open source. 2.8 trillion parameters. A 1 million token context window. It beats Claude Opus 4.8 and GPT-5.6. It sits on par with Fable 5. Fable 5 results, at Sonnet pricing. The wall between you and a AAA game used to be a studio. Now it's a prompt.

Dep

99,083 次观看 • 26 天前

Claude Fable 5 makes new things possible. I one shot a full horror game with it. One prompt. I have never seen AI output a game like this. This is the leap I've been waiting for. 80.3 on SWE-Bench Pro vs Opus 4.8 at 69.2, the biggest jump I have ever seen on that benchmark. It passed GPT 5.5 in both coding and intelligence on Artificial Analysis. I already canceled my $200 ChatGPT Pro plan. It fixed a Bridge Voice bug in one shot that GPT 5.5, Opus 4.7, and Opus 4.8 all failed to crack for 2 months. 10/10. Best model I have ever used. Full review below.

Claude Fable 5 makes new things possible. I one shot a full horror game with it. One prompt. I have never seen AI output a game like this. This is the leap I've been waiting for. 80.3 on SWE-Bench Pro vs Opus 4.8 at 69.2, the biggest jump I have ever seen on that benchmark. It passed GPT 5.5 in both coding and intelligence on Artificial Analysis. I already canceled my $200 ChatGPT Pro plan. It fixed a Bridge Voice bug in one shot that GPT 5.5, Opus 4.7, and Opus 4.8 all failed to crack for 2 months. 10/10. Best model I have ever used. Full review below.

BridgeMind

19,020 次观看 • 2 个月前

BREAKING: Claude-2, Anthropic's ChatGPT competitor was just released and it's incredible. It's cheaper, stronger, faster, can handle PDFs, and supports longer conversations. Highlights: 1. Claude is 5x cheaper than GPT-4. 2. It has more recent data. A a mix of websites, licensed data sets from third parties and voluntarily-supplied user data from early 2023. 3. It outperforms GPT4 on the GRE writing and HumanEval coding benchmarks. 3. It features a context window of 100,000 tokens, the largest of any commercially available model. 4. It can analyze roughly 75,000 words, about the length of “The Great Gatsby". 5. It can easily handle any code related tasks.

BREAKING: Claude-2, Anthropic's ChatGPT competitor was just released and it's incredible. It's cheaper, stronger, faster, can handle PDFs, and supports longer conversations. Highlights: 1. Claude is 5x cheaper than GPT-4. 2. It has more recent data. A a mix of websites, licensed data sets from third parties and voluntarily-supplied user data from early 2023. 3. It outperforms GPT4 on the GRE writing and HumanEval coding benchmarks. 3. It features a context window of 100,000 tokens, the largest of any commercially available model. 4. It can analyze roughly 75,000 words, about the length of “The Great Gatsby". 5. It can easily handle any code related tasks.

Lior Alexander

255,648 次观看 • 3 年前

Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.

Introducing Agent Mode: Agentic AI is now measured in the Arena. Agent Mode can do deep research, create reports, generate images, build websites, debug code, and more. It completes more complex tasks by using tools like web search, bash in a sandbox environment, image generation, file writing, and asking follow-up questions. Frontier models are waiting for you in Agent Mode to take on real-world tasks. GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and top open models. Test them yourself.

Arena.ai

411,466 次观看 • 2 个月前

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

Found a way to get $125-$200 in FREE ai credits for gpt-5.6 sol, claude opus 5 and opus 4.8 😳 agentrouter is a non-profit version of openrouter. one api key, no card. credits land on your account at signup. what you get for $0: -$125-200 in credits on signup (github only) -claude opus 5, gpt-5.6 sol, opus 4.8 through one key -free models -openai-compatible (works with anything) what this replaces: -paid frontier API costs for coding agents -claude opus 4.8: $15/M in, $75/M out -gpt-5.6 sol: $25/M out how to grab yours (2 min): 1. go to → sign in with github (important) 2. console → create your api key 3. base url: 4. pick a model and go Works in hermes, claude code, cursor, aider and any openai-compatible tools Important: github-only signup, older accounts get the full credit, fresh ones may get less. credits are one-time. referral adds +$100 use this before they tighten the offer

painn

107,318 次观看 • 9 天前

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Sam Altman, CEO of OpenAI: "GPT 5.6 is 54% more efficient on agentic coding, and it's still the best on the market for half the cost." AI spend has never mattered as much as it does today. As a result the models keep getting better and cheaper at the same time. This is going to widen the gap between the people using AI agents and everyone else, because now it's not just powerful, it's cheap. That's exactly why i wrote a full step by step guide on agents, what they are and how to build your own from scratch. Read it below.

Anatoli Kopadze

91,518 次观看 • 1 个月前

🚨 do you understand what happened to Claude.. Anthropic just shipped Opus 4.8 with something called dynamic workflows. It no longer works alone - it spins up hundreds of agents that argue, verify, and break each other's work until the answer is right. - A dev used it to port Bun from Zig to Rust in 11 days - 750,000 lines of Rust, 99.8 percent of tests passing - Hundreds of agents ran in parallel with two reviewers per file - Fast mode runs the same model 2.5x faster and 3x cheaper than before work you used to plan in quarters now finishes before the weekend

🚨 do you understand what happened to Claude.. Anthropic just shipped Opus 4.8 with something called dynamic workflows. It no longer works alone - it spins up hundreds of agents that argue, verify, and break each other's work until the answer is right. - A dev used it to port Bun from Zig to Rust in 11 days - 750,000 lines of Rust, 99.8 percent of tests passing - Hundreds of agents ran in parallel with two reviewers per file - Fast mode runs the same model 2.5x faster and 3x cheaper than before work you used to plan in quarters now finishes before the weekend

BuBBliK

431,325 次观看 • 2 个月前

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,950 次观看 • 1 年前

We just unveiled V4 — our new Enterprise Agent Framework — enabling teams to build voice and chat customer service agents with complete control. Agents now have Playbooks and Workflows, giving them the ability to execute complex agentic tasks, or follow scripted deterministic workflows in order. It’s faster than ever, powered by our industry-leading, multimodal Context Engine, ensuring customer conversations sound smooth and natural. And it’s even easier to measure ROI with LLM evaluations and deep visibility into transcripts. V4 is available now as early access. Visit the link to get your invitation:

We just unveiled V4 — our new Enterprise Agent Framework — enabling teams to build voice and chat customer service agents with complete control. Agents now have Playbooks and Workflows, giving them the ability to execute complex agentic tasks, or follow scripted deterministic workflows in order. It’s faster than ever, powered by our industry-leading, multimodal Context Engine, ensuring customer conversations sound smooth and natural. And it’s even easier to measure ROI with LLM evaluations and deep visibility into transcripts. V4 is available now as early access. Visit the link to get your invitation:

Voiceflow

660,002 次观看 • 5 个月前

When using GPT-5.5, it is instantly noticeable how much more powerful it is. In Codex, I gave it a very complex prompt to create London Toy Railway with landmarks and seasons - it did an excellent job in one shot. In the second half of the video you see GPT-5.4 - it was also not bad, but very clearly worse. GPT-5.5's generation is far more ambitious, coherent and with fewer errors. This is obviously a toy example, but I've used it on much more complex real tasks, including a complex app migration and a new hard workflow - it has been working away for many hours without getting stumped. I'm getting more and more addicted to this stuff with every model release.

When using GPT-5.5, it is instantly noticeable how much more powerful it is. In Codex, I gave it a very complex prompt to create London Toy Railway with landmarks and seasons - it did an excellent job in one shot. In the second half of the video you see GPT-5.4 - it was also not bad, but very clearly worse. GPT-5.5's generation is far more ambitious, coherent and with fewer errors. This is obviously a toy example, but I've used it on much more complex real tasks, including a complex app migration and a new hard workflow - it has been working away for many hours without getting stumped. I'm getting more and more addicted to this stuff with every model release.

Peter Gostev

262,507 次观看 • 3 个月前

FREE Claude Opus 4.8, GPT 5.5, GPT Image 2 and FLUX2 Pro. Berlin AI startup. No phone verification. MCP access included. The platform is Langdock. Enterprise AI workspace that passed Y Combinator and raised $3.5M. Real company, not a side project. Models are not all enabled by default - Opus 4.8 needs to be turned on manually in model settings. While you're there, enable Overusage too. Limits are solid, models are real. Here's the exact flow: → Go to langdock(.)com → Register with temp email or custom domain - no SMS verification → Enter any phone number - they don't send codes → Go to model settings and enable Opus 4.8 and other models manually → Check usage limits in the limits section MCP connection available - can connect to folders on your own PC. More details on the setup in my Telegram. End result: Claude Opus 4.8 and GPT 5.5 with MCP access to your local files. Free. One note: system prompt adds some restrictions out of the box - worth knowing before you start. Bookmark this before the free tier gets locked down.

FREE Claude Opus 4.8, GPT 5.5, GPT Image 2 and FLUX2 Pro. Berlin AI startup. No phone verification. MCP access included. The platform is Langdock. Enterprise AI workspace that passed Y Combinator and raised $3.5M. Real company, not a side project. Models are not all enabled by default - Opus 4.8 needs to be turned on manually in model settings. While you're there, enable Overusage too. Limits are solid, models are real. Here's the exact flow: → Go to langdock(.)com → Register with temp email or custom domain - no SMS verification → Enter any phone number - they don't send codes → Go to model settings and enable Opus 4.8 and other models manually → Check usage limits in the limits section MCP connection available - can connect to folders on your own PC. More details on the setup in my Telegram. End result: Claude Opus 4.8 and GPT 5.5 with MCP access to your local files. Free. One note: system prompt adds some restrictions out of the box - worth knowing before you start. Bookmark this before the free tier gets locked down.

Atenov int.

27,414 次观看 • 1 个月前

I got to try Grok 4.5 in early access in Cursor for the past few days and I absolutely enjoyed it. It feels like Opus 4.8 at 2x the speed at a much cheaper price point. I tasked it to brainstorm > plan > implement a big feature for my game (this act 1 boss fight) and it did not disappoint. - It is much smarter than Composer 2.5, during planning mode, it is able to think through my request more robustly, ensuring that edge cases are covered and makes sure to ask the right questions to confirm with me first. - It is much better at brainstorming ideas/suggestions, similar to Opus 4.8, though I think Fable still edges out a little when it comes to brainstorming ideas and suggestions - It is FAST. probably the fastest of all frontier models (Opus 4.8, GPT 5.5 etc), which makes it a joy to build with, because I can stay in the flow - It has much improved visual/animation capabilities than Composer 2.5, it can code up animations (i wanted an explosion animation with particle effects) with much, much better visuals, animation movement and timing. This is a big leap and I was so happy to see this improvement. - The best part for me is that I can just use the same model from planning down to execution without switching to a lower cost model because the price point is cheaper than other frontier models. I'll be testing this model with more challenging tasks in the next few days but I think this is going to be my main driver for vibe coding for a while. Also, its nice to see Grok back in the race. 🙌

I got to try Grok 4.5 in early access in Cursor for the past few days and I absolutely enjoyed it. It feels like Opus 4.8 at 2x the speed at a much cheaper price point. I tasked it to brainstorm > plan > implement a big feature for my game (this act 1 boss fight) and it did not disappoint. - It is much smarter than Composer 2.5, during planning mode, it is able to think through my request more robustly, ensuring that edge cases are covered and makes sure to ask the right questions to confirm with me first. - It is much better at brainstorming ideas/suggestions, similar to Opus 4.8, though I think Fable still edges out a little when it comes to brainstorming ideas and suggestions - It is FAST. probably the fastest of all frontier models (Opus 4.8, GPT 5.5 etc), which makes it a joy to build with, because I can stay in the flow - It has much improved visual/animation capabilities than Composer 2.5, it can code up animations (i wanted an explosion animation with particle effects) with much, much better visuals, animation movement and timing. This is a big leap and I was so happy to see this improvement. - The best part for me is that I can just use the same model from planning down to execution without switching to a lower cost model because the price point is cheaper than other frontier models. I'll be testing this model with more challenging tasks in the next few days but I think this is going to be my main driver for vibe coding for a while. Also, its nice to see Grok back in the race. 🙌

Danny Limanseta

1,417,825 次观看 • 1 个月前

FREE $100 in credits with access to Opus 4.8 and GPT 5.5. Sauna AI. No card. Gmail or Slack connect. Nobody is talking about this. Sauna AI gives you $100 in credits on signup. No card required. Just email plus Gmail or Slack connection - and Slack gets you more credits than Gmail. What's inside on the free tier: Claude Opus 4.8, Sonnet 4.6, GPT 5.5 and other current models. Limits are generous - not the usual "10 messages and you're out" situation. Real usage out of the box. Here's the exact flow: → Go to sauna(.)ai → Register with email → Connect Gmail or Slack when prompted - Slack gives more credits → Go to dashboard and start using 3 minutes. $100 in credits. Flagship models. No card. These promos disappear fast. Bookmark this post so you don't lose the link when it's gone.

FREE $100 in credits with access to Opus 4.8 and GPT 5.5. Sauna AI. No card. Gmail or Slack connect. Nobody is talking about this. Sauna AI gives you $100 in credits on signup. No card required. Just email plus Gmail or Slack connection - and Slack gets you more credits than Gmail. What's inside on the free tier: Claude Opus 4.8, Sonnet 4.6, GPT 5.5 and other current models. Limits are generous - not the usual "10 messages and you're out" situation. Real usage out of the box. Here's the exact flow: → Go to sauna(.)ai → Register with email → Connect Gmail or Slack when prompted - Slack gives more credits → Go to dashboard and start using 3 minutes. $100 in credits. Flagship models. No card. These promos disappear fast. Bookmark this post so you don't lose the link when it's gone.

Atenov int.

49,506 次观看 • 1 个月前