出海去孵化器 (@chuhaiqu)

每次有新技术，就有人说「这次不一样，人类要被淘汰了」。 农业机械化那次也一样。1/3 的人种地，现在只剩 2%，但产出翻了三倍。那些人没有失业，去了 1940 年根本不存在的行业。 a16z 的数据显示，公司用 AI 来增强岗位的频率，是替代岗位的 8 倍。所以其实核心问题只有一个：你有没有在用 AI 让自己的工作更值钱？

The top Hermes integrations to give your agent superpowers: 1. Firecrawl Basically web search built for agents. It's better than the native Hermes web search because it gives you clean web data, so responses come back faster and uses fewer tokens. I keep this on by default. 2. Browserbase Gives Hermes browser access for actually interacting with sites. Logging in, clicking buttons, booking stuff, anything that needs a real browser session. Hermes will automatically pick between Firecrawl and Browserbase depending on what the task needs, so you just plug both in. 3. Google Workspace Gmail, Calendar, Drive, Docs, and Sheets in one connector. If Hermes can't read your inbox, see your calendar, or write to your docs, it can't really work for you. Plug this in first. 4. Reddit The best signal you'll find on what people actually think about any product, niche, or problem (bc its real opinions from real users) Amazing for market research. 5. YouTube transcripts Pulls captions from any video. Long podcasts, tutorials, interviews etc become searchable notes in seconds. Probably the highest-leverage research integration nobody plugs in. 6. Discord I host my business in Discord, so this one's huge for me. I plug Hermes into different channels and have it run specific workflows in each. Example: I have a dedicated customer support channel where Hermes scans my email every morning for support tickets and drops them in organized. 7. GitHub Code, issues, PRs. Turns Hermes into an actual engineering teammate. Non-negotiable if you write code. 8. Stripe Payments, customers, failed charges, refunds. You can just ask "why did this customer churn" and get a real answer. Also can't wait for this...Stripe is releasing agentic payments, so soon Hermes will be able to actually book stuff with your card. 9. Bland (or Twilio) Gives Hermes a voice so it can place real phone calls (like booking reservations etc). I love listening to the recordings haha 10. Apify Pre-built scrapers for X, LinkedIn, Instagram, Google Maps, etc. The way to get X data without paying $5k/mo for the official API. 11. Readwise Every highlight you've ever saved from books, articles, tweets, and podcasts, all queryable. Solves the "dead knowledge" problem. 12. Granola (or Fathom) Searchable transcripts of every meeting you've had. Hermes can answer "what did that client say about pricing last month" instantly. 13. Obsidian For Karpathy LLM wiki second-brain maxxing. If I had to set up only 5, I'd do Firecrawl, Browserbase, Google Workspace, GitHub, and Obsidian. Covers ~80% of what most people need. I use Composio to add these in one click, makes setup basically zero effort instead of messing w technical stuff. Anything I'm missing?? What's in your stack?

100+ places to launch your startup: 1. Product Hunt 2. BetaList 3. TrustMRR 4. Uneed 5. TinyLaunch 6. Indie Hackers 7. Hacker News 8. Tiny Startup 9. PeerPush 10. SideProjectors 11. DevHunt 12. Launching Next 13. Microlaunch 14. Launch Directories 15. StartupBase 16. ShowMeBestAI 17. Trendy Startups 18. Software Advice 19. There's an AI for that 20. AlternativeTo 21. OpenAlternative 22. SaaSHub 23. Toolfolio 24. LibHunt 25. SaaS Genius 26. FoundrList 27. Stacker News 28. PitchWall 29. API List 30. MakerPad 31. Dan Recommends 32. Startup Buffer 33. AppSumo 34. SEO Wins 35. RocketHub 36. StackSocial 37. SaaS Mantra 38. SaaS Warrior 39. LTD Hunt 40. KEN Moo 41. Prime Club 42. SaaSZilla 43. Fazier 44. Peerlist 45. Next Gen Tools 46. Sustainability Softwares 47. Saas Baba 48. PromptZone 49. Futurepedia 50. Toolkitly 51. LaunchIgniter 52. Firsto 53. Indie Tools 54. Manta 55. Indie Deals 56. PayOnceUseForever 57. Slocco 58. ToolFame 59. GPTStore 60. AlterOpen 61. SaaS Gallery 62. Aura Plus Plus 63. That AI Collection 64. BasedTools 65. SaaS Pirate 66. Product Canyon 67. Deal Mirror 68. Dealify 69. Goodfirms 70. AI Agent Store 71. BroUseAI 72. Altern 73. BestWebDesignTools 74. MadGenius 75. BotsFloor 76. AIDir Wiki 77. Look AI Tools 78. The AI Generation 79. Waild World 80. Wavel 81. Indie Products 82. Invent List 83. Hack the Prompt 84. Startup Heroes 85. AI Marketing Directory 86. RankYourAI 87. EarlyHunt 88. Tekpon 89. Dokey AI 90. Appscribed 91. Open Tools 92. SEOFAI 93. Startups FYI 94. AI Tool Trek 95. Powerusers 96. AI Parabellum 97. Serchen 98. RobinGood 99. Affiliate Watch 100. IndieHunt 101. Reviano 102. Nocode List 103. Software World 104. AIxploria 105. Ctrlalt 106. AI Hunter 107. Public APIs

Announcing the Artificial Analysis Coding Agent Index! Our new coding agent benchmarks measure how combinations of agent harnesses and models perform on 3 leading benchmarks, token usage, cost and more When developers use AI to code they’re choosing a model, but also pairing it with a specific harness. It makes sense to benchmark that combination to understand and compare performance. The Artificial Analysis Coding Agent Index includes 3 leading benchmarks that represent a broad spectrum of coding agent use: ➤ SWE-Bench-Pro-Hard-AA, 150 realistic coding tasks that frontier models struggle with, sampled from Scale AI’s SWE-Bench Pro ➤ Terminal-Bench v2, 84 agentic terminal tasks from the Laude Institute and that range from system administration and cryptography to machine learning. 5 tasks were filtered due to environment incompatibility ➤ SWE-Atlas-QnA, 124 technical questions developed by Scale AI about how code behaves, root causes of issues, and more, requiring agents to explore codebases and give text answers Analysis of results: ➤ Opus 4.7 and GPT-5.5 lead the Index: Opus 4.7 in Cursor CLI scores 61, followed closely by GPT-5.5 in Codex and Opus 4.7 in Claude Code at 60. GPT-5.5 in Cursor CLI follows at 58. ➤ Open weights models are competitive, but still trail the leaders: GLM-5.1 in Claude Code is the top open-weight result at 53, followed by Kimi K2.6 and DeepSeek V4 Pro in Claude Code at 50. These are strong results, but still meaningfully behind the top proprietary models. ➤ Gemini 3.1 Pro in Gemini CLI underperforms: Gemini 3.1 Pro in Gemini CLI scores 43, well below where Gemini 3.1 Pro sits on our Intelligence Index, highlighting that Gemini’s performance in Gemini CLI remains a relative weak spot for Google’s offering. ➤ Cost per task (API token pricing) varies >30x: Composer 2 in Cursor CLI is cheapest at $0.07/task, followed by DeepSeek V4 Pro in Claude Code at $0.35/task and Kimi K2.6 in Claude Code at $0.76/task. At the high end, GPT-5.5 in Codex costs $2.21/task, while GLM-5.1 in Claude Code costs $2.26/task. For both models this was contributed to by high token usage, and in GPT-5.5’s case by a relatively higher per token cost. ➤ Token usage varies >3x: GLM-5.1 in Claude Code uses the most tokens at 4.8M/task, followed by Kimi K2.6 at 3.7M/task and DeepSeek V4 Pro at 3.5M/task. GPT-5.5 in Codex uses 2.8M tokens/task, substantially more than Opus 4.7 in Claude Code at 1.7M/task. In GLM-5.1’s case, higher token usage, cost and execution time were partly driven by the model entering loops on some tasks. ➤ Cache hit rates remain high but vary materially: Cache hit rates range from 80% to 96% across combinations. Provider routing, harness prompt structure and cache behavior can materially change the economics of running the same model given cached inputs are typically <50% the API price of regular input tokens. ➤ Time per task varies >7x: Opus 4.7 in Claude Code is fastest at ~6 minutes/task, while Kimi K2.6 in Claude Code is slowest at ~40 minutes/task. This is contributed to by differences in average turns per task, token usage and API serving speed. Opus 4.7 had materially lower amount of turns to complete a task than all other models while Kimi K2.6 had the most. ➤ Cursor made real progress with Composer 2: Composer 2 in Cursor CLI scores 48, near the leading open-weight model results, while being the cheapest combination measured at $0.07/task. Cursor has stated Composer 2 is built from Kimi K2.5, showcasing they have made substantial post-training gains. This is just the start. We are planning to add additional agents (both harnesses and models). Let us know what you would like to see added next.

每次有新技术，就有人说「这次不一样，人类要被淘汰了」。 农业机械化那次也一样。1/3 的人种地，现在只剩 2%，但产出翻了三倍。那些人没有失业，去了 1940 年根本不存在的行业。 a16z 的数据显示，公司用 AI 来增强岗位的频率，是替代岗位的 8 倍。所以其实核心问题只有一个：你有没有在用 AI 让自己的工作更值钱？

The top Hermes integrations to give your agent superpowers: 1. Firecrawl Basically web search built for agents. It's better than the native Hermes web search because it gives you clean web data, so responses come back faster and uses fewer tokens. I keep this on by default. 2. Browserbase Gives Hermes browser access for actually interacting with sites. Logging in, clicking buttons, booking stuff, anything that needs a real browser session. Hermes will automatically pick between Firecrawl and Browserbase depending on what the task needs, so you just plug both in. 3. Google Workspace Gmail, Calendar, Drive, Docs, and Sheets in one connector. If Hermes can't read your inbox, see your calendar, or write to your docs, it can't really work for you. Plug this in first. 4. Reddit The best signal you'll find on what people actually think about any product, niche, or problem (bc its real opinions from real users) Amazing for market research. 5. YouTube transcripts Pulls captions from any video. Long podcasts, tutorials, interviews etc become searchable notes in seconds. Probably the highest-leverage research integration nobody plugs in. 6. Discord I host my business in Discord, so this one's huge for me. I plug Hermes into different channels and have it run specific workflows in each. Example: I have a dedicated customer support channel where Hermes scans my email every morning for support tickets and drops them in organized. 7. GitHub Code, issues, PRs. Turns Hermes into an actual engineering teammate. Non-negotiable if you write code. 8. Stripe Payments, customers, failed charges, refunds. You can just ask "why did this customer churn" and get a real answer. Also can't wait for this...Stripe is releasing agentic payments, so soon Hermes will be able to actually book stuff with your card. 9. Bland (or Twilio) Gives Hermes a voice so it can place real phone calls (like booking reservations etc). I love listening to the recordings haha 10. Apify Pre-built scrapers for X, LinkedIn, Instagram, Google Maps, etc. The way to get X data without paying $5k/mo for the official API. 11. Readwise Every highlight you've ever saved from books, articles, tweets, and podcasts, all queryable. Solves the "dead knowledge" problem. 12. Granola (or Fathom) Searchable transcripts of every meeting you've had. Hermes can answer "what did that client say about pricing last month" instantly. 13. Obsidian For Karpathy LLM wiki second-brain maxxing. If I had to set up only 5, I'd do Firecrawl, Browserbase, Google Workspace, GitHub, and Obsidian. Covers ~80% of what most people need. I use Composio to add these in one click, makes setup basically zero effort instead of messing w technical stuff. Anything I'm missing?? What's in your stack?

Announcing the Artificial Analysis Coding Agent Index! Our new coding agent benchmarks measure how combinations of agent harnesses and models perform on 3 leading benchmarks, token usage, cost and more When developers use AI to code they’re choosing a model, but also pairing it with a specific harness. It makes sense to benchmark that combination to understand and compare performance. The Artificial Analysis Coding Agent Index includes 3 leading benchmarks that represent a broad spectrum of coding agent use: ➤ SWE-Bench-Pro-Hard-AA, 150 realistic coding tasks that frontier models struggle with, sampled from Scale AI’s SWE-Bench Pro ➤ Terminal-Bench v2, 84 agentic terminal tasks from the Laude Institute and that range from system administration and cryptography to machine learning. 5 tasks were filtered due to environment incompatibility ➤ SWE-Atlas-QnA, 124 technical questions developed by Scale AI about how code behaves, root causes of issues, and more, requiring agents to explore codebases and give text answers Analysis of results: ➤ Opus 4.7 and GPT-5.5 lead the Index: Opus 4.7 in Cursor CLI scores 61, followed closely by GPT-5.5 in Codex and Opus 4.7 in Claude Code at 60. GPT-5.5 in Cursor CLI follows at 58. ➤ Open weights models are competitive, but still trail the leaders: GLM-5.1 in Claude Code is the top open-weight result at 53, followed by Kimi K2.6 and DeepSeek V4 Pro in Claude Code at 50. These are strong results, but still meaningfully behind the top proprietary models. ➤ Gemini 3.1 Pro in Gemini CLI underperforms: Gemini 3.1 Pro in Gemini CLI scores 43, well below where Gemini 3.1 Pro sits on our Intelligence Index, highlighting that Gemini’s performance in Gemini CLI remains a relative weak spot for Google’s offering. ➤ Cost per task (API token pricing) varies >30x: Composer 2 in Cursor CLI is cheapest at $0.07/task, followed by DeepSeek V4 Pro in Claude Code at $0.35/task and Kimi K2.6 in Claude Code at $0.76/task. At the high end, GPT-5.5 in Codex costs $2.21/task, while GLM-5.1 in Claude Code costs $2.26/task. For both models this was contributed to by high token usage, and in GPT-5.5’s case by a relatively higher per token cost. ➤ Token usage varies >3x: GLM-5.1 in Claude Code uses the most tokens at 4.8M/task, followed by Kimi K2.6 at 3.7M/task and DeepSeek V4 Pro at 3.5M/task. GPT-5.5 in Codex uses 2.8M tokens/task, substantially more than Opus 4.7 in Claude Code at 1.7M/task. In GLM-5.1’s case, higher token usage, cost and execution time were partly driven by the model entering loops on some tasks. ➤ Cache hit rates remain high but vary materially: Cache hit rates range from 80% to 96% across combinations. Provider routing, harness prompt structure and cache behavior can materially change the economics of running the same model given cached inputs are typically <50% the API price of regular input tokens. ➤ Time per task varies >7x: Opus 4.7 in Claude Code is fastest at ~6 minutes/task, while Kimi K2.6 in Claude Code is slowest at ~40 minutes/task. This is contributed to by differences in average turns per task, token usage and API serving speed. Opus 4.7 had materially lower amount of turns to complete a task than all other models while Kimi K2.6 had the most. ➤ Cursor made real progress with Composer 2: Composer 2 in Cursor CLI scores 48, near the leading open-weight model results, while being the cheapest combination measured at $0.07/task. Cursor has stated Composer 2 is built from Kimi K2.5, showcasing they have made substantial post-training gains. This is just the start. We are planning to add additional agents (both harnesses and models). Let us know what you would like to see added next.