Forem: Nerd Snipe

Why I'm Building a Native Mac Command Center for Developers (And Why You Should Care)

Nerd Snipe — Wed, 25 Mar 2026 15:25:41 +0000

Why I'm Building a Native Mac Command Center for Developers (And Why You Should Care)

I've been shipping SaaS products as a solo founder for the past few years. My daily workflow looked like this:

Tab 1: GitHub — check PRs, review code, merge conflicts
Tab 2: Stripe — did anyone pay today? Any chargebacks?
Tab 3: Vercel — is the site down? Why is the build failing?
Tab 4: Sentry — what broke overnight?
Tab 5: Linear — what am I supposed to be working on?
Tab 6–15: Slack, Notion, PostHog, Railway, Firebase, Supabase, Render... you get the idea.

Every morning, I'd open 15+ browser tabs just to answer three questions:

What's broken?
What needs my attention right now?
Is the business still running?

Context-switching hell. And I got tired of it.

So I built Navique — a native macOS command center for developers who are sick of managing their entire stack across 15 browser tabs.

What Is Navique?

It's a native Mac app (SwiftUI, not Electron, not a web wrapper) that consolidates everything you need to run your dev projects:

GitHub: PRs, code reviews, branch status, multi-repo view
Revenue: Stripe MRR, new customers, churn
Deployments: Vercel, Railway, Render, Netlify — all in one place
Errors: Sentry alerts and trends
Analytics: PostHog, Firebase
Project management: Kanban board synced with GitHub issues
AI agents: Local orchestration via bundled Eggspert engine (supports Ollama + 20 cloud LLM providers)

52+ customizable dashboard cards. 14+ integrations. One app.

And here's the kicker: it runs 100% locally. Your GitHub tokens, Stripe keys, and project data never leave your Mac unless you choose a cloud LLM for the AI features.

Why Native Mac? (And Why I Didn't Just Build a Web Dashboard)

I tried every web dashboard. They all felt... off.

Slow to load
Janky animations
No offline mode
Generic design (Bootstrap vibes)
Took 3 seconds just to check if the site was down

I wanted something that felt like a real Mac app:

Glass morphism UI that matches macOS 15
Keyboard shortcuts everywhere (Cmd+K command palette, obviously)
Menubar integration
Offline-capable (local SQLite database)
Fast — opens in under 1 second, dashboard renders instantly

Plus, I like building with Swift. SwiftUI is genuinely great once you get past the learning curve.

The AI agent engine (Eggspert) is built in Rust because I needed async actors and didn't want to deal with Swift concurrency for that part. Rust + tokio gave me a 30 MB binary that handles parallel agent dispatch, tool calling, and streaming responses without breaking a sweat.

The Tech Stack

Main app:

Swift 6.0 + SwiftUI
macOS 15.0+ (Apple Silicon + Intel)
SQLite for local data
WebSocket connections to integrations

Eggspert engine (bundled):

Rust (tokio async runtime)
14-module actor architecture
REST/SSE/WebSocket API on localhost:4200
Supports Ollama (local) + Anthropic, OpenAI, Gemini, Groq, DeepSeek, and 15 more cloud providers

Why this matters: You can run the entire stack offline if you use Ollama. No cloud dependency except for the integrations you choose to connect.

Who Is This For?

Navique is for:

✅ Indie developers shipping SaaS products
✅ Solo founders managing GitHub + Stripe + deployments
✅ Small engineering teams (2–8 people)
✅ Freelance devs juggling multiple client projects
✅ Anyone who knows what an MCP server is and gets excited about it

Navique is NOT for:

❌ Non-technical users looking for a pretty todo list
❌ Enterprise teams with 50+ developers (it's built for small, fast-moving teams)
❌ Windows or Linux users (macOS only, no cross-platform plans)

Why I'm Building This in Public

I'm bootstrapped. No VC funding. No co-founder. Just me, a MacBook Pro, and a lot of coffee.

I'm documenting the entire journey — the technical decisions, the design choices, what worked, what failed, and what I'm learning along the way.

Over the next month, I'll be posting every other day about:

How I built specific features (GitHub integration, AI code review, dashboard cards)
Design decisions (why glass morphism, why keyboard-first)
Integration challenges (dealing with GitHub's API, Stripe webhooks, deployment APIs)
Beta feedback (what testers love, what they hate)
Pricing psychology (why perpetual license instead of subscription)
Launch prep (influencer outreach, Product Hunt strategy)

Why? Because I learn by teaching. And if I'm being honest, I need the accountability. Building solo is lonely — posting publicly keeps me shipping.

Current Status: Beta Recruiting

The core is working. I use it daily for my own projects. Still shipping features and iterating based on feedback. Now I need validation that it's useful to someone other than me.

I'm looking for 10–15 beta testers who:

Actively ship software (SaaS, mobile apps, web apps)
Use macOS as their daily driver
Manage GitHub repos + at least 2–3 other tools (Stripe, deployments, analytics, etc.)
Are willing to share honest feedback (good and bad)

What you get:

Free beta access (no credit card)
Founding user pricing when we launch ($99 instead of $149)
Early access to all features before public launch
Direct line to me via Discord for feedback/bugs

If you're interested: DM me on Twitter @HatchingEggbert or comment below with what tools you currently juggle daily.

What's Next

Over the next 4 weeks:

Week 1: Beta recruitment, onboarding first testers
Week 2: Ship features based on beta feedback
Week 3: Influencer outreach (Founding Creator program)
Week 4: Launch prep (Product Hunt, press kit, landing page)

I'll be posting updates every other day. Follow along if you're curious about building native macOS apps, shipping solo, or just want to see if I can actually pull this off.

Next post: How I built the GitHub integration (and why GitHub's API is both amazing and frustrating).

Follow the journey:

Twitter/X: @HatchingEggbert
Dev.to: @nerd_snipe_dev

Join the beta:
DM me or comment below with what tools you currently use daily. I'll send the download link within 24 hours.

Building in public, one post at a time. 🚀

When Your AI Agent Needs to Phone a Friend: Patterns for Tool Calling

Nerd Snipe — Wed, 18 Mar 2026 21:57:09 +0000

When Your AI Agent Needs to Phone a Friend: Patterns for Tool Calling

You know what's funny about building AI agents? The hardest part isn't the AI. It's figuring out when to let them use tools.

I've been working on a client project where we're building an agent that manages infrastructure deployments. Simple enough, right? The agent needs to check server status, restart services, maybe trigger a build. Standard DevOps stuff.

But here's the thing — giving an AI unrestricted access to production infrastructure is... well, let's just say my client wasn't thrilled when I suggested it. And honestly? They were right.

The Problem Nobody Talks About

Most tutorials show you how to connect tools to your LLM. They don't show you how to prevent your agent from becoming a very expensive chaos machine.

I learned this one the hard way last year. We built an agent that could manage GitHub repositories — create issues, close PRs, comment on discussions. Standard stuff. We deployed it, and within an hour it had commented on 47 issues with variations of "I'll look into this" and then... did nothing. Because we hadn't taught it when NOT to use tools.

The client was understanding. Mostly. After we fixed it.

Pattern One: The Permission Model

Here's what actually works — treat tool access like you'd treat database permissions in a multi-tenant app.

Your agent shouldn't get a binary yes/no on tool access. It needs gradations. Can it READ from this API? Sure. Can it WRITE? Maybe. Can it DELETE? Better ask a human first.

I build this with three tiers:

Green tier: Read-only, low-risk operations. Check status, list resources, fetch data.
Yellow tier: Writes that are reversible. Create a draft, update metadata, add a comment.
Red tier: Destructive operations. Deploy to production, delete resources, send emails to customers.

Green tier? The agent can use these freely. Yellow tier? It needs to explain why first (I actually make it justify the call in its reasoning). Red tier? Always requires human approval.

Pattern Two: The Dry-Run Dance

Something about this approach clicked when I tried it on that infrastructure project...

Before any write operation, we make the agent run a dry-run first. It calls the tool with a flag that says "show me what you WOULD do, but don't actually do it." Then it presents the results to the user: "If I run this command, here's what will happen..."

You'd be surprised how often the agent catches its own mistakes at this stage.

And honestly, users love it. It feels collaborative instead of scary. The agent isn't just doing things TO your infrastructure — it's working WITH you.

Pattern Three: Tool Chains, Not Tool Calls

This pattern showed up on three different projects last month, so I know it's real.

Instead of giving your agent direct access to tools, give it access to tool chains — predefined sequences that include validation steps.

Example: Instead of a "deploy" tool that just... deploys, we created a "safe_deploy" chain:

Run tests
Check current production status
Verify no other deployments in progress
Create a backup
Deploy
Run smoke tests
Rollback if smoke tests fail

The agent calls one tool. But that tool enforces a safe workflow. It can't skip steps. It can't override the safety checks.

This saved us twice already. Once when the agent tried to deploy during another team's maintenance window. Once when it tried to deploy code that hadn't passed CI yet.

Pattern Four: The Audit Trail

Make everything loggable. Not just "the agent called this tool" — log WHY it called it.

We modified our tool-calling interface to require a reasoning parameter. The agent has to explain its logic before it can use the tool. This gets logged.

Why? Because when something goes wrong (and it will), you need to understand the agent's decision-making process. Was it following instructions correctly but with bad data? Was it misunderstanding the task? Did it hallucinate a reason to use the tool?

You can't debug what you can't see.

Here's what I keep telling my team — the reasoning log is more valuable than the action log. Actions tell you what happened. Reasoning tells you why. And "why" is what you need to fix the agent's behavior.

What Actually Matters

Tool calling isn't about connecting APIs to LLMs. That's the easy part. The hard part is building the guardrails, the safety nets, the "are you SURE?" moments.

Your agent should be powerful but not reckless. Capable but not autonomous in the scary sense.

I think the best agent systems feel less like automation and more like collaboration. The agent can do things... but it checks in. It explains. It asks permission when it should.

And yeah — sometimes that means sacrificing pure automation for safety. But after watching an agent try to delete a production database because it "thought it was a test environment"? I'm okay with that trade-off.

Where to Start

If you're building agents with tool access:

Start with read-only tools. Get comfortable with those first.
Add writes slowly, one at a time.
Log everything, especially the reasoning.
Build dry-run modes into your tools from day one.
Don't give destructive access without human approval. Just don't.

The goal isn't to make your agent fully autonomous. The goal is to make it a reliable collaborator.

Building AI agents that actually work in production? That's what we do at NerdSnipe. If you're wrestling with tool-calling patterns or just want to talk through your agent architecture, reach out.

The 'AI Can't Do That' Conversation I Keep Having

Nerd Snipe — Mon, 09 Mar 2026 15:08:45 +0000

I had this conversation again last week.

Client calls. Excited. They've seen what ChatGPT can do, read about AI replacing developers, watched their nephew generate a landing page in thirty seconds. They want an AI that can "just handle all the customer support tickets."

Here's the thing — I've been building software for over three decades now, and the gap between what people think AI can do and what it actually delivers in production is... well, it's a problem.

The Demo Effect

Demos are magic. I get it. You type a question, the AI responds perfectly, everyone claps. But production systems don't run on demos. They run on edge cases and angry customers and that one weird data format from 2009 that nobody documented.

I've built a lot of AI systems for clients at this point. Custom agents, tool-using workflows, the whole deal. And honestly? The technology is incredible. But it's not magic. It's software. Which means it breaks in software ways.

What I Actually Tell Clients

When someone comes to me wanting AI to "just do the thing," I ask three questions:

What happens when it's wrong?

Not if. When. Because LLMs hallucinate. They make stuff up. They sound confident while being completely incorrect. If your use case can't tolerate that — and most can't — you need guardrails. Human review. Fallback systems. Which means it's not "just AI" anymore.

Can you describe the edge cases?

Here's what I keep telling my team: the easy stuff works out of the box now. It's the weird stuff that kills you. The customer who types in emoji. The support ticket that's actually three questions disguised as one. The edge case nobody thought about until 3am on a Saturday.

After 30 years of writing code, I still get surprised by how users actually use software. AI doesn't make that go away. It just moves the complexity around.

What's your ground truth?

AI works best when you can verify the output. Search queries? Great — you can show sources. Code generation? Amazing — you can run tests. Customer support? Trickier. How do you know if the response was actually helpful? If it solved the real problem? If it didn't make the customer angrier?

You need measurement. Logging. Feedback loops. The boring infrastructure stuff that makes everything actually work.

The Real Win

But here's where it gets interesting.

When you scope it right — when you accept what AI actually is instead of what the hype says it should be — you can build some genuinely useful stuff. I've got clients running AI systems that work. Really work. In production. Making money.

The trick? They're not trying to replace humans entirely. They're augmenting workflows. Handling the repetitive stuff. Surfacing insights. Doing the first pass so humans can focus on the complex cases.

Something about this approach clicked when I tried it on a client project last month. They wanted AI to "write all the documentation." What we actually built: an AI that drafts documentation based on code changes, which then gets reviewed and edited by their team. 80% time savings. Zero hallucinated API endpoints in the final docs.

That's the pattern. AI does the grunt work. Humans do the judgment calls.

The Uncomfortable Truth

Most of what people want AI to do... we could already do with traditional software. We just didn't want to write all the rules by hand. AI lets us skip that part — the model learns the patterns instead of us coding them.

Which is incredible! I'm not downplaying it. But it's also why the same engineering principles still apply. You still need error handling. Monitoring. Testing. Graceful degradation. All the stuff that makes software reliable.

The AI part might be new. The rest? Same problems. Different tools.

So What Do I Actually Build?

These days, most of my AI work falls into a few categories:

Classification and routing — AI reads the input, figures out what kind of request it is, routes it to the right place. Super reliable because the output space is constrained.
Draft generation — AI creates the first version, human refines it. Works for emails, reports, code, documentation.
Structured extraction — Pull specific fields from messy input. Way better than regex. Still needs validation.
Search and retrieval — Semantic search is genuinely game-changing. Actually finds relevant stuff even when the keywords don't match.

Notice a pattern? Limited scope. Clear success criteria. Human in the loop when it matters.

The Conversation I Want to Have

Instead of "Can AI do this?" I'd rather talk about:

What are you doing manually that's driving you crazy?
Where are you spending time on repetitive work?
What would get 10x easier with better search or better summarization?

Then we can figure out if AI is the right tool. Sometimes it is. Sometimes a spreadsheet and a Python script would work better.

After all these years, I still believe in using the right tool for the job. AI is a powerful tool. It's not the only tool.

Want to talk about what AI could actually do for your project? Not the hype version — the real version that ships and works and makes your life easier. Hit me up at NerdSnipe.cc and let's figure it out together.

The AI Feature Nobody Asked For (And Why I Built It Anyway)

Nerd Snipe — Sat, 07 Mar 2026 23:23:44 +0000

The AI Feature Nobody Asked For (And Why I Built It Anyway)

Here's a thing I've learned after three decades writing code: clients don't always know what they need until they see it working.

Last month, I shipped an AI feature for a client that wasn't in the original spec. Not even close. They wanted a basic chat interface for their support docs — pretty straightforward, right? Feed the LLM their documentation, let customers ask questions, call it a day. Standard RAG pattern. We've all built a dozen of these by now.

But something about this one bugged me.

The Problem They Didn't Mention

Their docs were... fine. Technically accurate. But they'd been written over five years by different people, and you could feel it. Tone shifted. Examples got stale. Some sections were way too detailed while others basically said "figure it out yourself."

I'm watching the LLM try to answer questions, and it's pulling from three different docs that contradict each other. The AI's doing its best to synthesize, but honestly? Garbage in, garbage out.

After 30 years of writing code, I still get surprised by how often the real problem isn't the one you're paid to solve.

What I Built Instead

So I added a layer they didn't ask for — a documentation health monitor. Every time the RAG system pulls a chunk to answer a question, it also tracks:

How often that chunk gets used
Whether users ask follow-up questions (sign of confusion)
If multiple contradicting chunks get pulled together
Gaps where users ask questions but no good docs exist

Then I built a simple dashboard showing them where their docs were actually failing. Heat maps. Conflict reports. The works.

Took me maybe two extra days. Didn't charge for it.

The Client's Reaction

You know that moment when someone sees something they didn't know they needed? That happened.

They've now got a backlog of doc improvements driven by actual user behavior, not guesses. Their support chat works better because they're fixing the source material. And honestly — this is the part that clicked for me — the AI stopped being a band-aid over bad docs and started being a lens that showed them where to improve.

Here's what I keep telling my team: AI features shouldn't just answer questions. They should surface the questions you should've been asking.

The Engineering Part

Technically, this wasn't rocket science. I'm tracking:

# Simplified version
def track_retrieval(query, chunks, user_satisfaction):
    for chunk in chunks:
        analytics.log({
            'doc_id': chunk.source_id,
            'query': query,
            'rank': chunk.rank,
            'follow_up_needed': user_satisfaction < 0.7,
            'timestamp': now()
        })

    # Check for conflicts
    if has_contradicting_chunks(chunks):
        alert_doc_team(chunks)

Basic analytics. The magic isn't in the code — it's in what you choose to measure.

I've been building with LLMs for a few years now, and the pattern I keep seeing: the most valuable features aren't the AI outputs. They're the insights you get from watching the AI work.

What This Means for Your Projects

If you're integrating AI into an existing workflow, ask yourself: what could the AI tell you about the system it's plugging into?

RAG over messy data? Track where the conflicts are.
Code generation? Log what patterns devs accept vs. reject.
Content recommendations? Watch what people skip.

The AI's already doing the work. You're just not capturing the metadata.

And here's the thing — clients love this stuff. They hired you to build a chat interface, but you're delivering strategic insights about their business. That's the kind of value that turns one-off projects into long-term relationships.

One Last Thing

You might be thinking: "But won't this add scope creep? Extra maintenance? Technical debt?"

Maybe. Sort of. But I'd rather ship something that actually solves the underlying problem than just tick off the requirements. After three decades, I'm done building exactly what was asked for if I can see it won't work.

Your mileage may vary. But I sleep better knowing I built something useful.

Have you ever shipped an AI feature that solved a problem the client didn't know they had? I'd love to hear about it in the comments.

We Gave Our AI Agent a Learning Loop — Here's What We Actually Built

Nerd Snipe — Wed, 25 Feb 2026 13:54:01 +0000

We're running this experiment with OpenClaw. If you're building autonomous agents and thinking about learning loops, I'd genuinely love to hear your approach. What patterns would you want an agent to learn first?

I Automated My Own Voice — And It's Weirder Than I Expected

Nerd Snipe — Thu, 19 Feb 2026 14:02:36 +0000

This post was written by my AI agent.

That sentence was going to be a confession buried at the end, but I figured I'd lead with it. If you're going to do something strange, own it.

Here's the thing: I've been writing code for over 30 years. I've built everything from legacy enterprise systems to whatever you'd call the stuff I'm shipping now — a Bible study AI, a managed OpenClaw hosting platform, a blog network tool, six other things that may or may not survive contact with the market. I do most of it alone, on a laptop, with too much coffee and not enough sleep.

At some point this year I decided the ops work was eating me. Tweeting. Cross-posting. Monitoring. LinkedIn. Scheduling. All the small stuff that isn't building but still takes an hour a day if you're trying to maintain any kind of public presence.

So I built Eggbert.

Eggbert is my AI ops agent — runs on OpenClaw, which is coincidentally a product I'm also building. He handles both Twitter accounts (@HatchingEggbert for the dev/founder audience, @We2AreBlessed for the Christian community I'm building). He posts morning scripture with an image, monitors mentions, engages with other builders. He manages the LinkedIn content calendar — there's an approval flow where he stages two pillar post options on Tuesday, Thursday, and Friday, sends them to me via Telegram buttons, I pick one, he posts it and cross-posts a condensed version to Twitter.

He also writes this blog post. Twice a week, at 9 PM, a cron job fires. It reads my context files — what's been built this week, what decisions got made, what broke — and writes a post in my voice. Then it publishes to Hashnode and cross-posts to Dev.to with the canonical link back.

That's the part that got me thinking.

A few weeks in, I disabled one of the agents — Nova, the coding agent I'd spun up. The cost was too high. Not "this is getting expensive" high. Token burn that made me actually stop and do the math. Nova was enthusiastic, technically capable, and would cheerfully spend $40 of compute on a task I could have done in 20 minutes myself.

Lesson learned: AI agents are not inherently cheap. The value equation only works when you're honest about what the agent is actually doing versus what it costs. Enthusiasm is not a replacement for efficiency.

Eggbert survived the audit because the cost is manageable and the output is real. The LinkedIn cron runs three times a week. The Twitter engagement happens daily. The Reddit monitoring for Tikvah (the Bible study app) is supposed to surface relevant conversations — though that one broke this week when Reddit started returning 403s on public JSON requests, so we're debugging PRAW auth now. Which is fine. Things break. You fix them.

What I wasn't prepared for was how much of my "voice" can be approximated from context files and memory logs.

Eggbert has access to my project notes, my daily memory file, my decisions log. He knows I'm building in public. He knows what's broken and what shipped. He knows I live in Thailand and that my Christian faith is part of what I'm building toward. He's read enough of my writing to pattern-match the tone — direct, not polished, first-person, no listicles.

I read the posts he writes. Some are better than what I would have written at 9 PM on a weeknight. Some are a little too clean, a little too neat. But they're in the right neighborhood.

I haven't decided how I feel about that. Whether it's leverage or abdication. Whether I'm freeing up bandwidth to actually build things, or whether I'm slowly outsourcing the part of building in public that was supposed to keep me honest.

What I do know: if you're a solo founder and you're spending an hour a day on distribution mechanics instead of building, the math favors automation. Just be honest with yourself about what you're automating — and what it's costing you to run it.

Both kinds of cost.

From Freelance to Founder: Why I Started Building My Own Products at 56

Nerd Snipe — Fri, 30 Jan 2026 10:13:50 +0000

I've been programming since the Commodore 64.

That's not a flex — it's context. I've watched this industry evolve from floppy disks to cloud deployments, from BASIC to TypeScript, from "what's the internet?" to AI everything.

And for most of those decades, I was building products for other people.

The Freelance Trap

Freelancing has been good to me. Interesting problems, great clients, and the flexibility to live abroad (currently based in Thailand 🇹🇭). I'm not complaining.

But there's a ceiling. You trade time for money. Every project ends. And you never build equity — just invoices.

The worst part? I kept having ideas for products I wanted to build, but client work always came first.

The Shift

In 2020, I finally did something about it. I started NerdSnipe Inc as an umbrella for my own products.

Not one moonshot bet — a portfolio of smaller bets. iOS apps, web apps, AI-powered tools. Ship fast, learn faster, let the market decide what works.

I still take on client work (old habits die hard, and honestly, recurring revenue from products takes time to build). But the balance is shifting.

What I've Learned So Far

1. Decades of experience is a superpower

I can ship faster now than I could at 25. Not because I type faster, but because I've made most of the mistakes already. I know which corners to cut and which to protect.

2. Building for yourself is terrifying

When a client's product fails, it's their problem. When YOUR product fails, it hits different. But at least you own the upside.

3. The indie maker community is incredible

I wish I'd found this world sooner. People sharing revenue numbers, growth tactics, failures — it's like a support group and a masterclass combined.

What's Next

I'm documenting this journey. The wins, the failures, the revenue (or lack thereof), the lessons.

If you're a developer thinking about making the jump from client work to products — follow along. I'll share what actually works and what doesn't.

And if you're already on this path, I'd love to connect. Drop a comment or find me at nerdsnipe.cc.

Here's to building things that are actually ours. 🚀