@mardehaym The AI is 5%.
We build the other 95%.
Get in touch 👇
limestonedigital.com/contact?utm_so…
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Limestone Digital
58 posts
Limestone Digital
@LimestoneHQ
Limestone Digital is an AI-native engineering partner that embeds senior developers and AI architects directly into mid-market product teams to ship faster.
Get in touch → Katılım Mayıs 2026
21 Takip Edilen18 Takipçiler
You still use Claude Code like a developer in 2025.
Here's the 2026 (non-coder) cheat sheet:
1. Open the Claude desktop app.
2. Click "Code" (not Chat, not Cowork).
3. Sign up for a free GitHub account.
4. Drop your project folder into Code.
5. Select Opus 4.7 (not Haiku, not Sonnet).
6. Use this setup guide: claudecode.free
Claude now builds anything you describe in English.
But here's where the 2026 version gets powerful:
Before you prompt, flip these 2 settings:
1. Switch to "Bypass Permissions."
No more "Allow this?" 20 times per session. Vibecode 100x faster.
2. Create a "CLAUDE .md" in your project root.
Type: "Write down everything you've learned about this project." Claude remembers your fonts, colors, and structure. Forever.
Then stop typing 500-word paragraphs.
Screenshot instead.
Drag a screenshot of any site you love into Code.
Paste this instead:
"I do not know how to code. Build me something like this for [GOAL]. Make it work on mobile. Loop until it's right."
Claude reads your screenshot. It builds the site.
You check it on your phone. You screenshot every bug. You send a numbered list. Refresh. Repeat.
The secret is not knowing how to code anymore.
It is knowing how to brief, screenshot & loop. But to go even deeper, use my playbook: claudecode.free
(save this if you can't code - you won't need to)
Ruben Hassid@rubenhassid
English
Traditional agency model: 1 PM, 2 seniors, 3 mids, 2 juniors. 8 people. Lots of meetings. Slow feedback loops.
Our model: 2 senior engineers + AI-augmented workflows. That's the pod.
What changes:
> Code review cycles drop from 48 hours to 4 hours
> Boilerplate generation is instant (AI handles it, seniors verify)
> No junior ramp-up time. No knowledge transfer overhead.
> Direct client communication. No PM telephone game.
The result across 12 client engagements: 2x feature throughput at 60% of the traditional team cost.
Smaller team. Better engineers. AI handles the rest.
English
The AI is 5%.
We build the other 95%.
Get in touch 👇
limestonedigital.com/contact?utm_so…
English
Pattern we've seen 6 times this year:
PE firm acquires a company. Inherits an engineering team. Immediately does one of two things:
Option A: Keeps everyone, changes nothing. Burns $200K+ before realizing half the team can't work without the one architect who just left.
Option B: Cuts 40% of engineering on day one. Saves money for 90 days. Then the product breaks and they spend 2x rebuilding.
The 90-day fix that actually works:
> Week 1-2: Audit every engineer against production output, not titles
> Week 3-4: Map the actual dependency graph (who depends on whom)
> Week 5-8: Replace low-output roles with senior-only pods (this is what we do)
> Week 9-12: Measure. Adjust. Ship the backlog that's been stuck.
You don't need more devs or fewer devs. You need the right devs doing the right work.
English
The AI is 5%.
We build the other 95%.
Get in touch 👇
limestonedigital.com/contact?utm_so…
English
The first time a client fired us, I didn't sleep for three days.
Not because of the revenue. Because I genuinely believed we were doing good work. And we were. Just not the work they actually needed.
The gap was expectations. We were building what we thought was right. They were expecting something they never clearly articulated. And I never forced the conversation that would've surfaced the mismatch.
Classic failure: two smart groups talking past each other for 8 weeks until someone snaps.
That client leaving cost us about $30K/month in revenue. But it gave us something worth 10x that: the MOW.
Monthly Output Worksheet.
Every single month, before any code ships, the client and the pod agree on exactly what "done" looks like.
Deliverables, metrics, timelines. Written down. Signed off.
If we don't deliver what's on the MOW, you don't pay. Period.
That policy exists because of the client who fired us. Their frustration built the best system in our company.
I used to think losing a client meant failure. Now I think the failure is losing a client and learning nothing from it.
Every company has a scar that became a system. Ours is the MOW.
English
Limestone Digital retweetledi
We named our AI failure modes.
Not because naming things is fun. Because unnamed failures don't get caught in code review.
Four monsters every engineer at Limestone is trained to spot:
1. The Confident Liar. Hallucination with perfect posture. Suggests optimizedFetch() with complete certainty. That method doesn't exist. We lost 4 hours once because a mocked test couldn't catch a hallucinated API.
2. The Overfit Intern. Nails common patterns, collapses on anything specific to your system. Generates perfect React code. Your app uses a custom framework. Three hours of refactoring later, you're behind where you started.
3. The Productivity Mirage. A junior dev ships AI-generated code for 3 months. First production bug, they can't debug it. "I don't really know what this does." About their own code.
4. The Silent Security Leak. Developer pastes their entire .env file as "context for debugging." Including production database credentials. Sent to an external AI service.
Every PR at Limestone includes a Monster Check:
— No Confident Liar: all APIs verified against docs
— No Overfit Intern: follows our patterns, not generic ones
— No Productivity Mirage: author can explain the code
— No Silent Security Leak: no sensitive data exposed
The industry needs shared vocabulary for AI failure modes, not just shared hype about AI success stories.
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