here
Joined May 2012
OK so who is preordering the Duo tomorrow
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Sarah has always had some pretty interesting takes, excited to hear more from her regularly. Also the production value is great!!
“Do you think that getting married young has made you less ambitious?” Welcome to the first episode of SOTA Wives with Sarah Chieng (Head of DevX at Cerebras, ⁠@MilksandMatcha⁠) x Vivian Midha Shen (General Partner at Y Combinator, ⁠@vivianmshen⁠). In episode 1: finding your person before getting rich, turning down startup funding to finish MIT, what a YC partner sees in founders, and how a birthday post became “psychological violence.” 4:08 Finding your husband before the exit 8:48 Did we marry away our ambition? 18:04 The internet called Sarah's birthday “psychological violence” 20:47 Does your YC partner secretly think you’re doomed? 23:47 Building a startup with your ex-boyfriend 28:54 SF cat parties
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Dang didn't realize codex usage resets disappear at 9pm PT
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Can't wait for this era of babysitting to be over honestly
I’ve seen this firsthand: Vibe coding fails when you can’t tell the difference between a "best practice" on paper and real-world experience. Real experience means factoring in all the trade-offs, from speed and cost to safety, and making the pragmatic choice, which even the best models can’t do. Here are some examples: 1. Data migrations. The model proposed a fleet of parallel cloud functions and new insert endpoints, adding endpoint, invocation, logging, and telemetry costs. The database was the bottleneck anyway, so a single remote server would have been enough. An agent could run the migration script there, detect unexpected data formats, update the script, and continue migrating as it encountered edge cases. It would have shipped and run twice as fast at half the cost. 2. Database constraints. Models love putting every business rule and enum constraint in the database. That sounds robust, but it can make shipping painfully slow. Every new enum value or business rule change becomes another database migration. Application-level validation is usually enough. Otherwise, monitor for inconsistencies and alert. 3. Missing variables. Models often add fallback values when an environment variable is missing. It keeps the app running, which feels convenient, especially during local development. But if infrastructure forgets to set a required variable or uses the wrong name, the app can silently run in development mode. The safer behavior is to fail loudly at startup so the deployment will halt. Knowing how to code matters because you need to recognize when the model’s "best practice" is wrong and override it.
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The future is heading here: agents configuring agents. Expertise, even of this meta kind, is becoming accessible through AI.
Introducing Scout. Tell it the KPI you care about, like funded deposits, and it builds the agents, learns from every conversation, tests each change, and keeps improving that number on its own. You set the goal. Scout gets you there.
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Dynamic island is proving to be a winner
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Excited to be cooking with @coffeeflux2 again
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Giga Browser Agent We work on some of the largest enterprise AI deployments in the world and learned automation outcomes are more often first blocked by API access than they are blocked on model capabilities...
Introducing Giga's browser agent. Giga can now log in and completes tasks end-to-end, just like your human agents. It used to take months to get support APIs deployed for AI agents in the enterprise. Now these integrations can be built instantly.
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... what surprised us was teams at AI agent companies and enterprises can spend quarters just to get to the starting line.
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That's why we've invested in integrating browser capabilities deep into Giga's agent platform. One powerful horizontal capability to unlock the last mile of automation in some of the largest companies in the world.
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Albert Chuang retweeted
The X trolls have crossed over from basic engagement clout farming to felony blackmail extortion
This quoted post is unavailable.
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Thanks @harjtaggar for highlighting @GigaAI on @tbpn
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Huge feature for @GigaAI — Nasdaq tower ✅
Excited to be on times square thanks @Nasdaq for featuring us.
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🧵 Understanding this concept deeply is fundamental to what makes @GigaAI so special. It's one of the biggest reasons I joined (beyond getting the chance to work with @eshamanideep and @varunvummadi).
The “forward deployed” concept will cause harm to many startups that go down this path. When you are building the product, park yourself at customers’ office. But, the goal should be to build a great product that does not require you to forward deploy a person for the customer to be successful. If you solve problems with people, you will default to throwing bodies at problems and will build an inferior product. Your goal should be the opposite.
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And if you keep pushing the product forward, you should find you need a lot fewer FDEs than you thought. Much of the role becomes closer to solutions engineering, which is something familiar to B2B SaaS companies with an enterprise motion.
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But to get here you need to believe in LLMs enough and you need to be ambitious enough as a product leadership team. Is your team long LLMs or short LLMs?
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