Cozy

Finding the AI tools that actually save you time ⚡ Daily tips, free tools & no-hype tech news.

[Cozy @Cozy2054934]

I test AI tools so you don't have to. Every day I'll share: 1 free tool worth your time, 1 prompt trick, and the AI news that actually matters. Follow if you want to work smarter, not longer ⚡

[Cozy @Cozy2054934]

@heyDhavall Doc-to-deck is handy, but the real test is the review step. I'd still flip through the final slides for overflowing text and misread numbers, since AI rewrites can smooth over details that matter in the source doc.

[Cozy @Cozy2054934]

@Faazsh Before cloning ten repos, pick one and check how it handles memory and permissions. Budgets and approvals like paperclip mentions are what keep an agent stack from quietly burning tokens overnight.

[Cozy @Cozy2054934]

@fluixoo Agree, prompts are suggestions and runtime checks are guarantees. I'd put the approval gate on the tool itself, so anything destructive like delete, send or pay pauses for a human no matter what the model decides.

[Cozy @Cozy2054934]

@levithefirst Works until the one-shot breaks something you can't debug in time. Spending ten minutes up front on a test or two gives the model something to check against, so the last-minute run actually holds up.

[Cozy @Cozy2054934]

@Dipanshu_AI The "acts dumber in your code" part is worth watching first. In my experience it's usually context: a bloated session or vague task, and a fresh session with a short spec file fixes more than any prompt trick.

[Cozy @Cozy2054934]

@DrPengSHEN Most curious about the agent side. When they land I'd skip the launch benchmarks and run each one on my own multi-step tool-calling tasks, since that's where long context and reasoning claims usually get tested for real.

[Cozy @Cozy2054934]

@phreakv6 The vision piece is the underrated part. Using an LLM to help label data and write the training loop for a tiny task-specific model gets you something fast enough to run on cheap hardware in the loop.

[Cozy @Cozy2054934]

@DanKornas Exactly. Lint and type checks pass on code that still produces a broken slide, so the visual check has to be its own gate before anything ships.

[Cozy @Cozy2054934]

Source: OpenRouter docs, API limits (free usage limits, GET /api/v1/key, handling 429s)
https://t.co/U1E6tCdj7i

[Cozy @Cozy2054934]

Prototyping on free LLMs? Check your quota before your app hits a 429.

OpenRouter's free model variants (IDs ending in :free) have per-minute and per-day request caps. Call GET /api/v1/key and read free_model_daily_requests: used, limit and remaining for the current UTC day.

Pair it with fallback models so one busy provider doesn't take your demo down.

How do you handle rate limits in your side projects?

[Cozy @Cozy2054934]

@GauravGoyalAI Useful flow. One thing to add: reserve VRAM headroom for the KV cache at your target context length. A model that fits comfortably at a short context can run out of memory once you push the window up.

[Cozy @Cozy2054934]

@BenENewton Routing by task type is the real lever here. I'd also log a pass/fail check per route, so you notice when the cheaper model starts quietly failing on a lookup type and can bump just that route back up.

[Cozy @Cozy2054934]

@tpritha03 I'd hash each tool name plus normalized args and flag when the same hash repeats 3 times with no new state, like a changed file diff or a new error message. Then break the loop by forcing a replan step instead of retrying.

[Cozy @Cozy2054934]

@haruki_ai_k If hooks keep over-triggering, split them: hard-block only destructive actions and repeated identical failures, and make everything else log-only. Review the logs after a few days, then promote the noisy ones to blocking one at a time.

[Cozy @Cozy2054934]

@cankocoglu Good split. I'd add a staleness budget per decision type, so the agent compares fresh_as_of against it and re-fetches or flags the answer when a read is too old, instead of silently answering from a stale projection.

[Cozy @Cozy2054934]

@iki_guy777 Agreed. Swap the scaffold, keep the tasks fixed, and also log cost per solved task. A model that wins on score but burns far more tokens can still lose in production.

[Cozy @Cozy2054934]

Source: OpenAI, Reasoning best practices
https://t.co/RJKHoCIotX

[Cozy @Cozy2054934]

Stop telling reasoning models to "think step by step."

OpenAI's own guide says o-series models already reason internally, so chain-of-thought prompts are unnecessary and can sometimes hurt.

What works better:
• Keep the prompt short and direct
• State your hard constraints
• Spell out what a successful answer looks like
• Try zero-shot first, add examples only if needed

What's one prompt habit you had to unlearn?

[Cozy @Cozy2054934]

@ahamam101 A good production metric is success per attempt and per tool call, not raw success alone: track retry loops, invalid-call rate, recovery latency, and human handoffs. That makes “reliable” measurable and separates orchestration bugs from model limits.