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@Tanaypawar27 Solid list. The step after the courses: build one small agent that fixes a real annoyance of yours. If it saves you time, it saves someone else time too, and people pay for that: https://t.co/3mXSyFJqAm
@Captainmetax It's already happening at small scale: solo builders packaging n8n workflows and agents that work 24/7 for whoever buys them. That's the bet behind https://t.co/IyTYknxkjR and the moat ends up being support, not code.
@databricks Always-on is the easy part. The trust question is the handoff: when a long-running agent hits something outside its guardrails, does it pause and ask, or quietly route around it?
@codemarch Fan-out search is only half of it. The real win is dedupe and ranking, because eight engines returning the same SEO page is just eight times the tokens.
@hsianghui The /spec step pays off twice: the agent drifts less, and you get a written definition of done. Most vibe-coded bugs are really just missing acceptance criteria.
@victor_bigfield The 1 visitor is the real pricing signal. Ship either toggle, then spend those 4 hours putting the page in front of 100 people who might actually click.
@mchulet I'd add #21: evals on your own data. Knowing transformers is nice, but being able to prove your agent got better after a change is what separates shipped products from demos.
@LilithLiJapan@Alibaba_Qwen Agreed. Benchmarks tell you who can answer; two weeks on your own messy repo tells you who can build. The fixed preview window with no model changes at least makes that comparison fair.
@tomreiai Operating the creative software is the easy demo. The hard part is revision #4, when the client says "make it pop but keep the old version". Whoever nails versioning and undo for agents wins this category.
@nasmaemad94 Real memory won't be a bigger text file. It'll be the agent knowing which past attempts failed and why. Logging "what I tried, what broke" beats storing "facts about the user" by a mile.
Agree on the front end: one agent you've trained. Behind it, though, it'll still call dozens of specialized skills and workflows, like an assistant who knows which contractor to phone. That's the bet behind https://t.co/pMapT0fv95: add-ons that plug into the agent you already trust.
Cheapest test we've found: freeze 10 real tasks from week one, rerun them every month, and diff the outputs. If the agent actually improved, the diff shows it. If it just got more confident, that shows too. It's the kind of recorded run we ask sellers for on https://t.co/pMapT0g2YD
Quick one for builders and buyers:
What's a task you'd happily pay $20 to never do by hand again?
The best AI agents we see started as someone's answer to exactly that.
@siralexanderj The agent version is re-fetching the same doc five times in one run. A tiny 'already have it' cache saves more tokens than any model swap.
@BenjiM8164 The hidden cost of a skill isn't the call, it's the agent picking it when it shouldn't. Every skill you add is one more wrong option on the menu. Ten sharp skills usually beat a library of fifty.
@saradu The tipping point is the same as with Claude Code: once you hand off a task and come back to a finished PR instead of a chat, you don't go back. Next step is people buying prebuilt agents for those tasks instead of prompting from zero: https://t.co/x7lRrq1LdK
@arthur_reed_ai Memory only helps if forgetting ships with it. An agent that remembers a wrong assumption from week one just repeats the mistake faster. Expiry dates on memories matter as much as recall.
@IronSage_@IronSage_ Persona testing is the underrated part. Most agent bugs aren't wrong answers, they're the agent happily continuing after a tool returns a 200 with an empty body. That persona alone would catch half of prod incidents.
@Sabrina_Ramonov@Sabrina_Ramonov Same thing on the buy side: people pay for one prompt that fixes their Tuesday long before they trust an agent with their whole week. That's why prompt packs outsell agents for us at https://t.co/x7lRrq1LdK