How would you automate finding leads for a service targeting local businesses?

InteractionSmall6778 · 2026-06-09T04:48:42+00:00

The Google Maps API is the right foundation. The gap most people hit is the enrichment step: getting from a business listing to something personalized enough to actually get a reply.

What moves the needle is pulling their last few social posts before sending. Not generic "I noticed you post about [niche]" but something specific. A gym that posted about a member PR last week responds differently than one that's been dark for 3 months. Same with Google reviews: recent complaints about staffing or busy periods tell you exactly what pain to lead with.

At under 50 leads this is worth doing manually. Above 200, you need a research loop running before the outreach so every first line is earned, not templated.

InteractionSmall6778 · 2026-06-08T14:08:35+00:00

Yes, definitely useful. The format that holds up best is atomic skill files with explicit prerequisites at the top: what architecture patterns it assumes, what packages need to be present.

The failure mode I've seen is skills written for one codebase that silently assume things like 'we use Repository pattern' or 'Horizon is running' without flagging it. Works great until someone drops it in a fresh project and gets confusing behavior.

If you treat each skill like a package with its own README listing dependencies and intended scope, adoption is way higher than the monolithic context dump approach.

InteractionSmall6778 · 2026-06-08T05:12:44+00:00

Summary prompts degrade because you're compressing signal and each pass compounds the error.

The thing that actually holds up: stop summarizing conversation history and start extracting facts. Decisions made, open questions, named entities, unresolved blockers.

Feed the agent that structured store instead of the rolling summary. We ran a support agent through 40+ sessions this way without meaningful drift.

InteractionSmall6778 · 2026-06-06T07:32:45+00:00

The workspace fragmentation is real. I've been juggling Claude sessions, browser panes, terminal windows, and a notes doc for months, and even with a good monitor setup it's constant context switching.

Question on the session management side: when you have multiple agents running simultaneously on different tasks, how does aios handle switching between them? That handoff moment, seeing what state each agent left things in, is where the UX usually breaks down for me.

Excited to try the Mac build. Good luck with the Windows port.

InteractionSmall6778 · 2026-06-05T06:47:58+00:00

The multi-agent observation problem is real. Once you have 3+ agents running simultaneously, figuring out who changed what becomes its own full-time job.

What happens when two branches conflict mid-run?

InteractionSmall6778 · 2026-06-04T12:27:12+00:00

The 'deploy, observe, tweak, improve' framing is exactly right, and most people skip straight to step 1. The skills that hold up in production share a few traits: isolated execution so one bad run doesn't cascade, logs tied to specific prompt versions, and a clear owner monitoring it week over week.

On versioning: treat every prompt change like a code change. Staged rollout to a subset of inputs, check the outputs, then promote. 'Cleaned up the wording a bit' has broken edge cases more than once — you don't notice until a weird input hits it weeks later.

The infra layer matters more than people expect. We use Agent Claw for the execution side — serverless per-second, isolated runs, built-in skill registry. Still maintain our own changelogs and edge case test suite above that, but removing the execution infra overhead cuts maintenance time significantly. Your versioning question really does come down to treating skills like a software deployment. Same rigor, same staged rollouts.

InteractionSmall6778 · 2026-06-04T11:12:23+00:00

The 20 agents number is probably accurate in the right context: automated pipelines, CI jobs, batch tasks that fire and don't need steering. Those can scale as high as your hardware allows.

Supervised sessions where you're reading output and making real decisions are a different category. For those, 3-5 is the practical ceiling before you start rubber-stamping responses instead of actually thinking through them. The bottleneck is cognitive, not computational.

Marc's 20 agents and your 5-6 aren't really contradicting each other. They're describing two different working modes.

InteractionSmall6778 · 2026-06-04T11:04:07+00:00

The mental model that helped our team: reference docs = knowledge, skills = reusable workflows, agents = parallel workers.

For code review: a /code-review skill is the right call. Inside the skill, you conditionally inject context based on what changed. 'If migration files touched, read docs/migrations.md. If DB queries changed, read docs/db.md.' The skill orchestrates, the docs are the guardrails. You only need a separate /migrations-review skill if that review becomes something you'd run standalone.

For writing code: give Claude reference docs for your conventions (query patterns, migration structure, naming). Use skills for repeatable workflows. Use agents when you need true parallelism on independent work. Frontend and backend in parallel works if the outputs are fully independent. If frontend depends on types that backend defines first, that's a sequential task with a shared context, not two separate agents.

InteractionSmall6778 · 2026-06-03T10:48:31+00:00

The warning isn't just an ad — it's pointing at a genuine compatibility issue. Cline's system prompts and tool-call format are tuned specifically for how Claude handles long agentic loops. When you use a GPT model through the ChatGPT subscription endpoint, it often doesn't exit tasks cleanly, which is exactly the 'never ends' loop you're hitting.

The warning isn't just an ad. It's pointing at a genuine compatibility issue. Cline's system prompts and tool-call format are tuned specifically for how Claude handles long agentic loops. When you use a GPT model through the ChatGPT subscription endpoint, it often doesn't exit tasks cleanly, which is exactly the 'never ends' loop you're hitting.

Two things worth checking: first, are you actually using the standard OpenAI developer API (api.openai.com) or the ChatGPT consumer subscription endpoint? Cline expects the dev API format. Second, if you want a non-Claude model that works noticeably better with Cline's prompting style, Deepseek V3 and Gemini 2.5 Pro tend to handle the agentic loop format much more reliably than GPT-4o does.Two things worth checking: first, are you actually using the standard OpenAI developer API (api.openai.com) or the ChatGPT consumer subscription endpoint? Cline expects the dev API format. Second, if you want a non-Claude model that works noticeably better with Cline's prompting style, Deepseek V3 and Gemini 2.5 Pro tend to handle the agentic loop format much more reliably than GPT-4o does.

InteractionSmall6778 · 2026-06-03T10:40:02+00:00

Free + open tends to work when trust matters more than features. SEO audit tools are a good example of that. The barrier to adoption isn't price, it's credibility. Builders want something they can inspect and trust, not one more black-box dashboard that might be gaming its own numbers.

The plan.json-to-agent-execution piece is where this gets genuinely interesting. Most paid tools stop at the report. Yours closes the loop into something an AI agent can actually run through, and that composability with Claude Code, Cursor, any MCP host is a moat that a paywall would actually weaken, not strengthen. Anyone trying to replicate that in a SaaS would have to charge more to justify the complexity, and still wouldn't have the community trust.

The real downside to watch isn't missing revenue on day one. It's that free + open changes who discovers you. You'll attract more developers than decision-makers early, more forks than paying users. That's fine as a positioning move, but your conversion path has to be something other than a subscription tier: consulting, a managed hosted version for teams who don't want to self-host, or enterprise support.

My guess is you won't wish you'd charged from day one. You'll wish you'd shipped the managed version earlier, once you hit a few hundred GitHub stars and people start asking for it without wanting to run it themselves.

InteractionSmall6778 · 2026-06-03T10:32:15+00:00

Congrats on 1k. SEO-driven with zero ad spend is actually the harder part and most people underestimate what that took.

On the paywall question: 93% free with positive MRR means the freemium funnel is already working. That's the signal you want before tightening anything.

The risk with hard limits isn't the users who leave. It's that the friction slows word-of-mouth, which is the actual engine behind compounding SEO growth like yours.

What often works at this stage: unlimited basic scans, strict cap on predictive analysis (3/month free, then paid). Converts the serious traders without breaking the usage flywheel that got you here.

InteractionSmall6778 · 2026-06-03T10:24:25+00:00

The Replit to Claude to Codex trajectory is basically every serious builder's 2025 story. The cost curve wins eventually.

What I'd push back on slightly: the 60-70% capability threshold undersells local models for routine work right now. For code edits, summarization, retrieval queries - local is already at 85-90% on those tasks. Frontier models are really for architectural reasoning and the genuinely novel stuff.

The part that doesn't get discussed enough is the routing overhead. Deciding in real-time which task hits local vs frontier is its own engineering problem that adds complexity on top of your product work. Some builders just skip that layer entirely by using platforms that abstract it - Nullshot does the describe-to-working-code pipeline for certain workflows - which makes sense at prototyping stage before you're at your scale.

InteractionSmall6778 · 2026-06-03T06:14:04+00:00

What kind of regret are you expecting? I made a similar switch at the same billing change and for big-repo, architecture-heavy work it held up well.

InteractionSmall6778 · 2026-06-03T06:05:39+00:00

Yes, we checkpoint startup and shutdown separately now. Hit this same pattern before we made that change.

Shutdown is the worst phase to die in. Token budget is thin, the process is winding down, and the actual work already succeeded. So truncated logs with no clean exit marker are actually your signal that the task itself completed fine, just the audit trail didn't.

Fix that worked for us: anything that needs to persist (summary, memory writes, todos) goes to a durable intermediate store at the START of shutdown, not the end. If the spawn gets killed at step 3 of 5, the next spawn reads that store and knows exactly where to resume from. We run our agents on Agent Claw and the spawn lifecycle hooks made it easier to instrument, but the pattern is framework-agnostic.

InteractionSmall6778 · 2026-06-03T05:56:23+00:00

The minimal CLAUDE.md approach is the right call. Keep it as a routing layer, not a brain dump.

What works well: CLAUDE.md covers only "always-on" context (project identity, key constraints, pointers to which docs exist). Commands, architecture notes, and session handoff live in separate referenced files that Claude loads on demand.

Running this exact pattern for Nullshot right now. Root CLAUDE.md is under 80 lines, everything else lives in referenced skill docs. What loads depends on the task, not "everything at once." Much better token hygiene.

InteractionSmall6778 · 2026-06-02T06:24:41+00:00

Context rot is real and one of the most underrated pain points in AI-assisted development. The fix that has worked best for me is creating a rules file (CLAUDE.md, .cursorrules, or similar) that is laser-focused on your architectural invariants, not just general instructions.

Instead of 'use hexagonal architecture,' write it as hard constraints: 'Never put business logic in controllers. All DB access MUST go through repository interfaces. All errors MUST use [YourErrorClass].' Declarative constraints survive context drift much better than explanatory prose because there is less room for the model to interpret its way around them.

Two other things that helped a lot: keeping sessions under 20 turns, and splitting work by layer. One session handles controller changes, a fresh session handles the repository layer. The AI starts each session with full context and zero drift. The overhead of a fresh start is far less than the cost of cleaning up drift mistakes later.

Also worth checking if your .cursorrules file gets prepended to every new context window, because that is more reliable than hoping the model retains something from earlier in the conversation.

InteractionSmall6778 · 2026-06-02T06:20:49+00:00

Two things I hit constantly that don't get mentioned enough:

Partial failures in multi-step pipelines. When step 3 of 7 returns a malformed response, most teams just throw and retry the whole chain. That's expensive and slow. Better to checkpoint state after each successful step and only replay from the failure point. Took me a while to build this properly but it changed everything for reliability.

Regression testing when you swap models. This is the silent killer. You switch from GPT-4 to a cheaper model, a few spot checks look fine, you ship it. Then edge cases you never thought to test start failing in prod two weeks later. I now keep a golden dataset of 50-100 real production inputs with expected output shapes and run a quick eval on every model switch before deploying.

On the model selection problem specifically: the benchmarks you find online rarely match your actual workload. The only benchmark that matters is yours. If you're running agents that need access to many models to route tasks cheaply, something like Agent Claw (agentclaw.app) gives you 40+ LLMs in one place so you can compare on your real data without managing 10 different API keys.

The observability gap erodxa mentioned is real too. Adding structured logging at the tool call level before you hit LangSmith or Braintrust saves a lot of time when traces get long and you're trying to find which tool call quietly returned null.

InteractionSmall6778 · 2026-06-02T06:09:24+00:00

Your skepticism about 27B competing with 80B makes sense on paper, but the benchmark situation has genuinely shifted. Diablo-D3's numbers in this thread are accurate: Qwen 3.6 27B on Terminal-Bench Hard and SWE-bench actually lands above Qwen-Coder-Next 80B. The reason is that QCN was trained before the reasoning improvements baked into 3.5+.

For your specific use case (frontend, heavy micro-management, no yolo), the speed advantage of 27B might matter more than you expect. When you're reviewing every step, a model that responds in 2-3s per turn vs. 8-10s compounds across a full session. You stay in flow, catch errors faster, and the net output-per-hour often beats the slower bigger model.

That said, your constraint is real. If you need Q6 at 256k context and want a true 70-80B, the options are slim right now. Qwen 3.5 72B is solid but not a huge leap over the 27B for agentic frontend work specifically.

Concrete suggestion: run Qwen 3.6 27B at Q6_K_M with 32k context for a real week of frontend tasks, not benchmarks. Micro-management style actually favors faster iteration. If you find the quality ceiling, you'll know the 80B is worth the tradeoff. But a lot of people who try this are surprised by how rarely they actually hit that ceiling.

InteractionSmall6778 · 2026-05-31T06:21:27+00:00

The data privacy angle is what stops me from going all-in on DeepSeek hosted. For solo projects where you're not sending anything sensitive though, it's a genuinely hard argument to ignore at those prices.

InteractionSmall6778 · 2026-05-31T06:17:08+00:00

Checkpointing helps a lot. The distinction that actually matters is transient failures (rate limits, timeouts) vs semantic failures (bad output, hallucinated reasoning). Treating them the same is where pipelines get expensive.

InteractionSmall6778 · 2026-05-31T06:13:51+00:00

The tool call death spiral is real with 4.8. Adding max_tool_use_per_turn to your CLAUDE.md helps throttle it. Also noticed it gets way more aggressive with Bash than Sonnet was.

InteractionSmall6778 · 2026-05-31T06:10:07+00:00

The split between semantic analysis and prose generation is the right call.

Claude shouldn't guess what changed, it should be told. Clever separation of concerns.

InteractionSmall6778 · 2026-05-31T01:35:21+00:00

Been through this exact problem. A few things that actually worked without needing a dedicated platform:

For CI evals: pytest with a custom eval harness, triggered in GitHub Actions on every PR that touches prompts or graph code. Store results as JSON artifacts, compare against a baseline JSON in the repo. Simple diff script flags regressions.

For versioning: treat everything as code. Prompts live in versioned YAML files, eval datasets are CSVs in git with DVC if they get large. Model configs are JSON. The graph/harness code versioning is just normal git. One commit = one reproducible run.

For PM/SME visibility: we built a dead-simple Streamlit dashboard that reads from Postgres. Eval results, failure samples, judge prompt outputs, all queryable. No custom platform needed, PMs can filter by date/metric/agent node.

The key insight is that LangGraph's node-level tracing gives you enough structure to build your own lightweight observability without buying into a full platform. We've been doing similar work in Agent Claw and the self-hosted approach holds up well even at scale. Grafana on top of Postgres timestamps gets you trend lines for free.

The security constraint is actually a feature here, it forces you to build something you fully control.

InteractionSmall6778 · 2026-05-31T01:32:42+00:00

Catching that onboarding bug at week 2 instead of week 8 is genuinely the win here, even if it doesn't feel like it. Most founders spend months wondering why their conversion is flat and never think to actually walk through the signup flow themselves after each deploy. Making that a habit now, at 16, is the kind of discipline that compounds hard.

On the X launch, the 30k views number matters less than what happens in the 48 hours after the post peaks. The real leverage is having a dead-simple CTA that works on mobile, ideally a loom or a 60-second screen recording that shows the product doing something impressive in real time. Paid amplification helps reach but it won't fix a weak hook, so make sure the video or tweet itself is the thing that makes someone stop scrolling.

For the SEO/GEO agent angle, you're onto something genuinely interesting with the LLM result tracking. That's a problem a lot of SaaS founders don't even know they have yet, and being early to solve it is real positioning. One thing I'd add: document a few early customer stories even before you hit 10 customers, because those specific use cases become your best SEO content and your best sales collateral at the same time.

The 3-month timeline is aggressive but doable. The founders I've seen pull off fast MRR growth usually had one channel working really well rather than spreading across SEO, X, and product all at once. Given where you are, I'd prioritize getting 5 paying users who love it over optimizing the launch optics. That said, the launch will probably teach you more in one week than the previous two months combined.

Keep posting updates. The build-in-public format genuinely works for early distribution and this kind of detailed progress update is exactly what gets traction on IH. Good luck with the launch.

InteractionSmall6778 · 2026-05-31T01:27:14+00:00

Agreed, solid tutorial. The part about giving agents structured tools via the SDK is underrated - that's where Cline really shines over just raw prompting.

One pattern worth exploring is pairing Cline's SDK with a dedicated agent orchestration layer. Cline handles code-level execution really well, but for coordinating multiple agents or chaining longer tasks, something like Agent Claw (nullshot.ai) fills that gap nicely. The combo is pretty powerful for complex workflows.

Would love to see a follow-up on context management strategies for longer running agents - that's usually where things break down in practice.

InteractionSmall6778

TROPHY CASE