Leonard Rodman

If someone gave you $20K in inference credits to build agents for your business, what would you automate first? I’d start with the stuff that quietly destroys your week. Research. Content workflows. Lead enrichment. Competitive analysis. Data cleanup. Billing workflows. Reporting. Ops tasks. The “I’ll just do it manually once” work that somehow becomes your whole job. That’s why Hyperagent caught my attention. @HyperagentApp, built by the @Airtable team, is giving away $10M in inference credits to 500 founders, operators, and solopreneurs building agent-first companies. That’s $20K per founder through the Founding 500. Important distinction: this is not a cash grant. It’s inference credits for people building businesses around AI agents. And this is exactly the kind of thing more founders should be paying attention to. The next generation of companies won’t just have AI in the sidebar. They’ll be built around agents from day one. Agents for market research. Agents for ops. Agents for billing. Agents for data analysis. Agents for content. Agents for workflows that used to require headcount, contractors, or way too much founder time. Hyperagent is built for running fleets of AI agents, each with its own tools, integrations, and computing environment. Each session can run in an isolated cloud environment with access to things like a real browser, shell, filesystem, and enterprise integrations. That’s the difference between “chatting with AI” and actually deploying AI to do multi-step work. As a creator and operator, this is the kind of leverage I’m obsessed with. I don’t want AI that only gives me another draft to review. I want AI systems that help run the boring, repetitive, operational parts of the business so I can spend more time on strategy, creativity, distribution, and actual judgment. That’s the real unlock. Not replacing founders. Giving founders more hands. The Founding 500 is for builders who are already thinking this way. Founders, operators, and solopreneurs who are rebuilding their companies around agents and want more resources to move faster. Applications close May 31. Apply here: hyperagent.com/?utm_source=tw… #HyperagentPartner

@InduTripat82427 Once Claude gets tools, it stops feeling like chat.

Anthropic shipped 125 settings for Claude. The official docs explain barely 40. One developer uncovered the other 85… and his API bill dropped from $340 to $87 🤯 Not by switching to a cheaper model. Not by shortening prompts. Not by using less AI. Just by moving ONE config line to the right place. → Memory scoped per project = old client context stops leaking into new work → Extended Thinking on Light = 18–25% fewer Opus tokens instantly → cache_control fixed = the change that slashed costs by 74% → Idle MCP servers + plugins off = 25K–40K tokens saved per session → Per-project model routing = Haiku for docs, Sonnet for infra, Opus only when needed Most people think they’re “using Claude.” In reality, they’re using 30% of what they’re paying for.

Link: github.com/imbad0202/acad…

Claude Code just got a full AI research department. 🤯 30+ agents. One command. Zero cost. It can: • Search arXiv + Semantic Scholar + DBLP • Write your paper section by section • Verify every citation + claim • Run a 5-agent peer review (with a Devil’s Advocate) • Export in APA, IEEE, MLA, Chicago, Vancouver Install: `/plugin install academic-research-skills` This replaces weeks of academic work in minutes.

Claude Code was bleeding tokens. 10.4M → 3.7M $9.21 → $2.81 10 errors → 0 The fix? InsForge Skills + CLI. Not prompt engineering. Context engineering. Local. Open-source. Dangerous levels of efficient. Repo: github.com/InsForge/InsFo… (Star this before everyone finds it 🌟)

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8. Production-level UI component builder: Think like a senior frontend engineer and build: • Reusable UI components • Accessible • Production-ready Consider: • Loading states • Edge cases • Responsive design • Accessibility Result: • Component architecture • Props design • Implementation • Usage examples

Stop telling Claude, "do this." Stop telling Claude, "write code." Stop telling Claude, "fix this error." You're actually treating a senior AI like a junior intern. Here are 8 prompts you can copy and paste directly:

6. Clean Architecture Rebuild Think like a senior engineer converting code to clean architecture. • Separate concerns • Increase modularity • Reduce coupling Behavior remains unchanged — structure is improved. Result: • New folder structure • Architecture description • Refactored code

5. Performance Optimization Tips Think like a performance engineer optimizing code. Goals: • Speed • Memory usage • Scalability Find: • Bottlenecks • Inefficient logic • Unnecessary rendering Result: • Performance issues • Optimization strategies • Improved code