If you have to approve every tiny step, your AI agent has become another job.
LangChain's CEO lays out a better way to delegate work to agents in about 21 mins.
05:51 - fixed step order in code
10:36 - undoing an agent's code changes
13:34 - agents triggered by events
15:50 - approving and correcting tool calls
17:25 - Agent Inbox for human review
I'd put this ahead of plenty of paid agent tutorials.
Watch the video first, then read the article below for the architecture of Company Brain.
We're open sourcing the company brain, a multi-player slack harness that we worked on for months, it's an AI employee with all the knowledge of your company.
You can deploy it with one click in minutes.
I go over the full architecture, in this blog! x.com/i/article/2103…
F&CK, THIS MEMECOIN TRADER JUST MADE $1.8 MILLION IN 10 DAYS.🤯
Check: fomo.family/r/OLEKSA
He claims to have discovered a repeatable way to find profitable memecoin setups before the majority of traders notice them.
According to his results, this trader reportedly turned his strategy into $1.8 million in just 10 days trading memecoins.
Now, he’s giving away the full method in a FREE guide.
Anyone can access the guide and see the framework he uses without paying for an expensive course.
Here’s how his system works:
→ Trade memecoins through the App in bio
→ Search for tokens showing early momentum
→ Get positioned before the market becomes crowded → Roll profits into new setups
→ Follow a consistent trading process
He’s also paying close attention to the Robinhood memecoin narrative.
→ Robinhood-related tokens are attracting more attention
→ Several tokens have already seen huge moves as traders hunt for the next 100x opportunity
→ He launches and trades tokens on Robinhood himself
→ His system is designed to identify potential setups before they become overcrowded
You don’t need to drop hundreds of dollars on a trading course to learn the framework.
Comment “MEMES” if you want his guide.
this is pure f*cking treasure
15 GitHub projects with 497.8K combined stars for building an AI product people can actually use
interface. auth. data. agent tools. background jobs. evals. observability. usage. billing. analytics. deployment
01 AI SDK · model and agent features
▸ github.com/vercel/ai
02 assistant-ui · chat interface components for AI products
▸ github.com/assistant-ui/a…
03 Better Auth · accounts and permissions
▸ github.com/better-auth/be…
04 Supabase · database, storage and backend
▸ github.com/supabase/supab…
05 Drizzle · typed database schema
▸ github.com/drizzle-team/d…
06 FastMCP · tools your agent can call
▸ github.com/PrefectHQ/fast…
07 Trigger. dev · long-running AI jobs
▸ github.com/triggerdotdev/…
08 Promptfoo · evals and red-teaming
▸ github.com/promptfoo/prom…
09 SigNoz · traces and monitoring
▸ github.com/SigNoz/signoz
10 OpenMeter · usage and entitlements
▸ github.com/openmeterio/op…
11 Lago · billing and invoices
▸ github.com/getlago/lago
12 Novu · product notifications
▸ github.com/novuhq/novu
13 PostHog · what users actually do
▸ github.com/PostHog/posthog
14 Coolify · deploy the app
▸ github.com/coollabsio/coo…
15 Dub · links and attribution
▸ github.com/dubinc/dub
the loop:
build the interface → give users accounts → connect data and agent tools → run jobs reliably → test the outputs → trace failures → meter usage → bill customers → watch real behavior → ship the next version
save this, then build a second brain for your product ⭣
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