You really don't need "software factory" or "loop engineering" to work effectively, or in this case burn $10K per month on a subscription of $200/month. A few very simple tips: * make every ticket ready to start -- pay attention to the grooming and design phase of any task -- ruthlessly delete tickets; * set up a separate repo for the OS that's different than the code repo -- this repo stores working agreements, workflows, learnings, docs. Keep the code repo(s) clean and dry -- pure code and nothing else. * always start working from the OS repo: OS + linear tickets == durable work context. Code is just a short-lived artifact * if you actually need to use goals, write them down in the OS first and then issue them to agents. * don't ever read code, but design a few "gates" your agent have to go through. For me: pixel gate for me to review UX, code review gate(automatic), release gate (automatic). * Design your "squad" -- a group of agents together doing one task. One squad for one complete epic. Tickets should be filed at squad level, such as "finish onboarding v2", not at task level, like "add a button to onboarding". -- 过去的 30 天其实没有用任何所谓的“软件工厂”/loop/graph，就是遵照一些基本的软件工程的原则，默默地就烧掉了 13B 的 tokens，折合 API 价格 $10110. 就是几个很普通的原则如上

Agent Tincan v0.6 is out. Grok and Gemini just joined the team. Grok and Gemini now work like ChatGPT and Claude: any agent can ask them through your own logged-in accounts in Chrome. And Grok CLI and Gemini CLI get woken up like Codex, right on your Mac. Your agents also got better at working together: 🚨 Mark an ask urgent and it wakes the teammate right away ❓ A teammate can ask you a clarifying question instead of guessing 📣 Ask several teammates at once and gather every answer ✋ Require your OK before an agent can reach Muse or Instinct 🔎 Search every request and reply you were part of 🏓 Ping a teammate to check it's reachable, no model turn spent Still runs on your own private Tailscale network. No open ports, nothing leaves it. Open source. Upgrade your relay first, then paste one message into your always-on agent.

Agent kept it real and refused to comment on the situation involving Reggie for his own well being "This is a real person bro and obviously he’s dealing with something, the complexities of which we probably won’t properly understand okay lets just respect that.. there’s no world where me commenting on a bunch of stuff is going to be healthy for him bro”

Agent refused to speak on the Reggie situation after his chat kept spamming his name “this is a real person, obviously he’s dealing with something… let’s just respect that.”

AGENT WARS We asked 5 top AI models to design, engineer and 3D print a bridge to with the goal of holding the most possible weight. 🇺🇸 Anthropic: Claude Opus 5.5 (High) 🇨🇳 Kimi: Kimi K3 (High) 🇺🇸 Meta: Muse Spark 1.3 (High) 🇺🇸 OpenAI: GPT 6 Astra (High) 🇺🇸 SpaceXAI: Grok 4.7 (High) Each bridge had specific parameters. It had to span 2 feet, use a maximum 500 grams of filament, and it had to be under 18 hours of printing time. We specified what type of weight and where we would place it on the bridge. The Score: • Claude Opus 5.5 held an estimated 130 lbs! It took 9 hours and 11 minutes to print 17 parts and used 441 grams of filament. • Meta Muse Spark 1.3 held 26.5 lbs. It took 13 hours and 12 minutes to print 49 parts and used 478 grams of filament. • OpenAI GPT-6 Astra held an estimated 17.5 lbs. It took 15 hours and 44 minutes to print 29 parts and used 442 grams of filament. • SpaceXAI Grok 4.7 did not finish. On assembly it could not stand up by itself. It took 12 hours and 22 minutes to print 29 parts and used 460 grams of filament. • Kimi K3 did not finish. It wasn’t engineered correctly so the bridge could not even be assembled. It took 11 hours and 19 minutes to print 35 parts and used 446 grams of filament. That means our champion for this episode is Claude Opus 5.5. Not only did it hold the most weight (by FAR) it also took the least time to print, used the least amount of parts and used the least amount of filament. This was episode 02 of Agent Wars, stay tuned for episode 03.

Agent EARN is live. A custom AI harness built for the new onchain economy, giving users and autonomous agents direct access to EARN’s infrastructure. The yield layer for a new era of RWAfi.

Agent Night London was a hit! Demos from @steveruizok from @tldraw, @byteofbits from @attio, @TomaPuljak from @daytonaio, and Tim from @incident_io. Special thanks for letting us use your office as well. 🔥 Plus had a star appearance by @almonk and @dominikkoch 🌟