Building an AI-native services company. Would love to connect with founders, agency owners, and industry folks to learn, collaborate, and grow together. #LetsGoX

You’re interviewing a developer. They finish your take-home assignment using Claude Code. Every test passes. They wrote almost none of the code. What’s the next question you ask before deciding whether to hire them?

Before you blame the model, read the input it got. Most "the agent is dumb" bugs I've chased were the agent being handed a stale record, a truncated doc, or last week's prompt. Log the exact input on every run. Most mysteries end there.

Your AI API returns no answer. You still get billed. For reasoning models, max_output_tokens covers hidden thinking AND visible text. Set it too low and the model may stop before printing a word. Check status: incomplete, then leave room for the answer.

Your AI app feels slow. Switching models might not fix it. Measure 2 things: TTFT = time until the first token (queueing + prompt processing). ITL = time between streamed tokens. vLLM exposes both. If TTFT spikes, inspect the queue before tuning generation.

Your AI agent shouldn’t use vector search to answer “How much did we sell yesterday?” That’s a SQL question. DuckDB queries Parquet files directly: SELECT SUM(amount) FROM 'sales.parquet' WHERE day = CURRENT_DATE - 1 Use embeddings for similarity; SQL for exact totals.

Two coding agents editing one folder can overwrite each other's work. Use Git worktrees: git worktree add ../feature -b feature Each agent gets a separate directory and branch, sharing one repo. Edits stay isolated; review and merge them when done.

anyone here building ai agents for boring back-office work? invoices, intake, follow-ups. what's the one task clients actually pay for? drop it 👇 lets #connect

Giving an AI agent 80 tools upfront is like importing your entire codebase to call one function. OpenAI Tool Search can load definitions on demand: add tool_search and mark rarely used functions defer_loading: true. Less context wasted; one extra discovery step.

Before you add an LLM step to a workflow, try writing it as a rule first. If the rule fits in one line, you don't need a model there. You need an if statement. The check I run on every step: 1. Input is structured (a status, an amount, a form field): rule 2. Output is one of a few known values: rule or a lookup table 3. Input is messy (an email thread, a PDF, a phone photo of an invoice): model 4. A wrong answer costs money or trust: model drafts, a person approves Rules are cheap per run, fast, and fail the same way every time, so you can actually test them. Model steps are where the cost, the latency and the weird 3am failures live. Spend them only where something needs reading or judgment. A good agent is mostly boring code with a few smart steps in it. Not a model in a loop deciding everything.

Google monetized what you search. Meta monetized what you scroll. Personal AI bots could monetize what you’re about to do. Imagine brands bidding to become your AI’s recommendation. The next trillion-dollar ad industry might not sell impressions. It might sell decisions.

Git should require a doctor's note for commits made after 2 a.m. "I know what I'm doing" is not a valid medical diagnosis.

My bank wants 12 characters, an uppercase letter, a special symbol, and my grandmother's birth certificate. Meanwhile eMudhra, a company trusted to secure HTTPS: private_key = 4 Apparently the strongest encryption is confidence.

Personal branding has gone too far. Apparently your existential crisis is off-brand. Please have your breakdown in the approved shade of blue.

You're probably using 10% of your ChatGPT subscription. One of its most underrated features is Scheduled Tasks. ChatGPT Pro lets you run up to 15 active tasks. Think of that as 15 AI research assistants working for you, even when you're offline. Here's what you could have them doing: 🔍 Research & Intelligence Monitor your industry for emerging trends every morning. Track competitors, product launches, pricing changes and new features. Scan Reddit, G2 and forums for customer complaints and unmet needs. Discover SaaS opportunities from existing products with frustrated users. 💼 Business & Career 5. Find potential customers matching your ideal customer profile. 6. Research companies and identify buying signals. 7. Discover new job openings matching your skills and preferences. ✍️ Content & Marketing 8. Research trending topics and draft X/LinkedIn posts. 9. Track what people are discussing in your niche and suggest content ideas. 👨‍💻 Development 10. Monitor GitHub repositories, issues, PRs and releases. 11. Research new developer tools, libraries and AI models. 📬 Personal Productivity 12. Summarize important emails and flag those needing replies. 13. Prepare meeting briefs using your calendar and relevant context. 14. Summarize Slack discussions, decisions and blockers. 15. Deliver a personalized daily briefing covering everything you care about. And here's the part most people miss: ChatGPT has connectors (apps/plugins). You can connect supported services like Gmail, Google Calendar, Google Drive, Slack and GitHub. So your scheduled tasks aren't limited to generic web research. They can work with information from your actual tools, subject to permissions. Some tasks can even react to incoming emails, Slack messages or GitHub activity. You don't need to remember to ask ChatGPT the same questions every morning. Set up the tasks once. Let them research, monitor and bring useful information to you. Stop treating ChatGPT like a chatbot. Start treating it like a team.

Archaeologists in 3026 will find our startup landing pages and conclude humanity went extinct in private beta.

be honest: vibe coding is hard yes????

Demo: “It booked the meeting.” Production: “It booked the meeting three times and emailed the wrong person.”

Before an agent gets write access to real systems, these four things have to be true: > Every tool call has an idempotency key > It re-checks state immediately before writing (not what it read earlier) > Timeouts are treated as “unknown,” not “failed” > Risky actions require explicit human approval Smarter models don’t replace any of these.