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Supabase Compute lets you run long-running serverless workloads (chunking, embedding, agent jobs) with direct Postgres connections, and it is not limited to Deno — Rust, Python, or full containers can run inside it.
Use sharding to scale OLTP Postgres into the petabyte range rather than trying to make Postgres an analytical database. Treat analytics workloads separately from transactional (OLTP) workloads — use the right technology for the right job. Splitting transactions across two servers forces fetching from object storage, which will eventually slow down your database. You can still store data directly on object storage and read from it via a different route, just don't fuse OLTP and analytics into one system.
Start prototypes on Supabase Spark (SQLite-compatible small instance) and upgrade to full Postgres when the project takes off — one database for any idea. Zero-downtime migrations between backends are a need-to-have, not a nice-to-have, because apps can spike from zero to front-page-of-Hacker-News traffic overnight.
Supabase's big focus this year is moving to code-first, agent-native: launching a Supabase instance on your local machine gives you a folder representing your database. Use the Supabase CLI to get a virtual file system representation of your database — infrastructure as code. Make database schema fully declarative in files (tables, policies, comments) so agents can iterate fast on the file system instead of reaching out to a running database. Supabase is releasing pg-donta, a declarative schema library usable with any Postgres database.
Set the incline bench to 60 degrees, use light weights, start with elbows back behind the body, curl by bending only at the elbow while twisting the dumbbell into supination, allow the elbows to come forward slightly with an extra twist at the very end, then come down slow and re-establish elbow positioning, contracting the triceps at the bottom for a deeper biceps stretch.
Perform the preacher curl on the same 60-degree bench angle, press the entire back of the triceps against the pad, push back into the pad to engage the lats and support the shoulder girdle, keep tension in the bottom position, and twist the wrist into supination as much as possible at the top.
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Tereza recommends three levers for saving money on AI: use caching to stop prefilling context every time, set no limits on spending but require people to show results when they spend a lot, and use smart routing between LLMs to optimize tokens instead of spending so many tokens.
Roman Ugarte describes manually onboarding 200-300 early users, sitting on a 20-minute call watching for confusion or technical issues, and immediately fixing the worst pain point so the next onboarding goes better.
Roman Ugarte recommends starting completely from scratch rather than adding features to an existing product, and creating a small team that goes off into a cave for about a month with a single objective, completely isolated from the rest of the company.
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Roman Ugarte describes a two-week period of intensive manual onboarding where the team onboarded 200-300 early users via calls. Whenever they observed an onboarding pain point, they fixed it immediately - within 24 hours - because the next person would be onboarded the following day.
Tejas Bhakta says you really need to make sure your agent is aware of the hardware — e.g., on a B200, the warps, Tmem, TMA, and so on. These low-level operators change generation to generation (H200 won't have Tmem, which is new with B200), which is why this context is needed. In practice this is a bunch of MD files you give the agent so it has context.
Tejas Bhakta advises that the human's job when reading a profiler is to look at the top and identify dumb approaches — for example, recognizing that loading 32K chunks into context is unnecessary for DeepSeek attention and should only be done every 32K instead. At a high level, all you have to tell autoresearch is that the top method is dumb and to pipeline it instead; everything else like sizing, chunk sizing, and context chunks should be decided by autoresearch.
Tejas Bhakta says you also need to make sure your agent has context of the model — every new model like DeepSeek Flash comes out with new tricks, such as DeepSeek v4's two new attentions: compressed sparse attention and hierarchical compressed attention. If you don't give the agent model context, he says the model will 100% hallucinate the actual attention mechanism and you will get useless kernels.
Nouha Dziri endorses replacing Sol sub-workers with cheap Luna workers in the planning-heavy setup, which yields almost the same performance at 60% less cost, losing only a little performance. A strong lead with cheap workers (Sol + Luna) achieves the same performance as Sol + Sol but at much lower cost.
Nouha Dziri recommends splitting the work across a team: a lead keeps the task and hands out subtasks, and each worker starts with a fresh context. Each worker can read files, run tests, and dig through logs, then send back only a compact result, so the lead keeps the full picture and can finish faster.
To optimize the agent setup for good reward at much cheaper cost, Nouha Dziri recommends taking the same lead and giving it workers at only one stage of the software engineering process.
The narrator advises owning the page that an AI Overview quotes, because that link sits above every blue result. The new job in AI search is getting named and getting cited: if you get named in an AI answer's short list, you've been recommended to a buyer who hasn't even clicked yet.
To get started with the Figma GitHub app, connect your Figma organization to your GitHub organization or account, select the GitHub account or organization to connect, and choose whether to authorize all repositories or a select set. The first few steps are the same for all Figma admins, and the repository authorization choice can be changed later.
A step-by-step walkthrough for connecting a Figma organization to GitHub: open Admin from the file browser, go to Settings > Resources, select Figma GitHub App, click Connect GitHub, and install the app from the Connect to GitHub modal.
After installation you can manage which repositories the Figma app can see: from your GitHub account, navigate to settings, click GitHub Apps, select Configure next to the Figma app, and under Repository Access choose All Repositories or select repositories, then click Save. To use repositories with code layers in Figma Make, you must allow cloning of repositories.
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