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@madappgang

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Get this sh*t done ☄
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AI / ML / LLM wizards | Scale-ups lifeline
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Yes — We did ask @chatgpt to make @jackrudenko into an action figure in a blister pack.

🤜 🤛 Jack isn’t just a CTO.
He’s the tech lead you call when you want your infrastructure to scale like it’s on rocket fuel—and stay secure like Fort Knox.

In every project:
– He architects the unbreakable.
– He leads teams that actually want to follow.
– He makes you believe in «10x growth» and then casually delivers 100x.

👀 The difference?

While others sell slides, Jack ships results.
MadAppGang doesn’t “ideate.” We don’t drown you in decks.
We take your product and drag it, kicking and screaming if necessary, all the way to launch.

⚡ No fluff. No excuses. Just shipping sh*t that scales.

Looking to scale like mad?  📈
🫳🏻 Go to our website | Link in BIO

#madappgang #businessbuilding #scaleyourbusiness Yes — We did ask ChatGPT to make Jack Rudenko into an action figure in a blister pack. by @madappgang

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2 years ago

It has been 10 years since Eugene Simonov, our Backend Developer, joined @madappgang

In an industry where devs jump every year…
This matters more than people think.

It’s about:
→ avoiding the same mistakes twice
→ understanding why decisions were made (not just what was built)

That’s how systems stay alive when things scale.

This is what our clients actually buy.
Not just development.

But:
→ continuity & context
→ systems that don’t collapse under pressure

One team. Built to stay.

If your product is starting to outgrow its original architecture —
you need people who’ve seen what happens next. Let’s talk. by @madappgang

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6 months ago

This repo from @github just hit 119k stars 🔥 it solves the problem every team hits when they move from one AI-assisted file to an entire AI-built product.

Vibe coding works for a demo. On a real project the agent drifts, forgets context, and over-engineers things you never asked for. Spec Kit fixes this by forcing a plan before any code gets written.

The whole flow is five slash commands inside whatever agent you already use:

→ /speckit.constitution sets your project’s principles
→ /speckit.specify captures what you’re building and why
→ /speckit.plan adds the tech stack and architecture
→ /speckit.tasks breaks it into ordered, parallel-safe steps
→ /speckit.implement builds the feature against the plan

And it’s agent-agnostic. Copilot, Claude Code, Cursor, Gemini, Codex, 30+ in total, so you keep your tools and gain the structure.

We run Claude Code across 70+ engineers. The pattern Spec Kit enforces: spec first, plan second, implement third - is the same discipline that keeps agent output reliable at scale.

Check out the repo here: https://github.com/github/spec-kit

📌 Save this for later by @madappgang

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2 months ago

@microsoft laid off 13,000 engineers over the last 12 months while simultaneously increasing spend on AI and cloud infrastructure.

That’s not a contradiction. That’s the transition playing out in real time.

The teams being cut are not the teams building the orchestration layer, the agent infrastructure, the cloud platforms that everything else runs on. The teams being cut are the ones doing work that the new infrastructure layer is absorbing.

This is the same pattern we see at the scale-up level. Engineering organisations that built their stack on manual processes, rigid pipelines, and monolithic systems are hitting the ceiling at exactly the moment the tooling exists to replace that ceiling with something better. The ones that don’t make that transition don’t get a second restructuring warning.

The engineering work that survives this shift is the work that sits one layer below what AI can automate: the orchestration layer, the infrastructure, the systems that the agents run on top of. That’s the layer worth building right now.

Follow @madappgang for the architecture patterns we run across 70+ engineers and 100+ production projects. by @madappgang

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2 months ago

We tested three LLM expanders on 860 real code search queries. All three made results worse. Here is exactly why…

Bigger model: down 28.6%. Fine-tuned specifically for the task: still down 26.4%. The full four-component pipeline degraded MRR@10 by 40.6% and pushed P95 latency to over 20 seconds.

The only thing that helped was a regex classifier that runs in under 1ms.

The reason: most code search queries in AI agent sessions are symbol-name lookups. BM25 handles these directly — the identifier appears verbatim in the source file. LLM expanders rewrite the query into a semantically richer version with low lexical overlap with the actual class definition. The right result drops out of the top 10.

The optimal pipeline has no LLM at query time. A regex classifier routes symbol lookups to BM25-only search and everything else to hybrid retrieval.

📌 Save this for later. by @madappgang

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3 months ago

These 2 Senior Engineers at @airbnb will teach you more about Agentic Coding than 100 YouTube video guides.

@airbnb shipped one of the most ambitious LLM-agent migrations in production. This is the 15-minute breakdown of how they actually did it.

We run @claudeai across 70+ engineers and 100+ production projects. What these engineers describe maps directly to what holds up under real client pressure.

Follow @madappgang for more production signal on agentic development. by @madappgang

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3 months ago

Something spent $400 in tokens last Tuesday. Which agent did it, and what exactly was it doing?

Agent frameworks ship you into production with no answer to that question.…

The carousel above maps exactly which frameworks close that gap, LangGraph’s time-travel debugging, Dify’s built-in LLMOps dashboard, Mastra Studio’s execution traces and evals, and which ones leave you reaching for Langfuse as a bolt-on.

📌 Save this for later by @madappgang

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3 months ago

Why do so many CEOs come from engineering backgrounds?

@jeffbezos studied computer science.
@satyanadella started as an engineer.
Jensen Huang has a degree in electrical engineering.

In AI it looks the same: Dario Amodei, @alexanddeer , Ilya Sutskever — all technical founders running the companies defining the industry.

This is not random.

The old model rewarded management skills and financial optimization. That worked when products evolved slowly. Modern tech companies succeed or fail on execution speed, technical trade-offs, and product quality. Those decisions require someone who understands the technology, not someone who receives a translated summary of it.

As our CEO Anton Kalinin puts it — and Jensen Huang said it first: when leaders do not understand the technology, companies stop being product-led and start becoming sales-led.

The numbers reflect it:

→ Engineering is the most common degree among Fortune 500 CEOs
→ 1 in 3 top-performing CEOs have engineering degrees (Harvard Business Review)
→ Technical backgrounds are especially common among leaders of large tech companies (2023 S&P 1500 report)

This trend has existed for years. In AI and deep tech it is accelerating, because the distance between vision and execution is too small to have a translation layer in between.

At @madappgang , engineering has been at the top since day one.

What do you think, does technical background actually change how leaders make product decisions? by @madappgang

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3 months ago

MULTI-AGENT ORCHESTRATION
CHEAT SHEET

📌 Save & repost this cheat sheet to find this later by @madappgang

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3 months ago

The decisions that do not matter at one agent become load-bearing at three, critical at ten, and existential at fifty.

Memory persistence. Shared context layer. Cost attribution per agent. Human-in-the-loop gates. Durable execution that survives failures without losing state.

None of these show up on a framework feature checklist. All of them determine whether your system holds in production.

The carousel above walks through each transition point and the specific architectural decision that becomes critical at each stage — built around our full framework evaluation of 20+ tools.

📌 Save this for when you are past agent three. by @madappgang

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3 months ago

A senior engineer on @reddit with 10 years of experience just explained the one skill he’d teach every vibe coder.

It quietly saves money on every project:

Get the agent to write plain deterministic code, then run that code for free.

Most engineers who came up through vibe coding have only ever worked on the non-deterministic side. Every problem gets a prompt. Every run burns tokens. The deterministic side of the stack - the part that runs the same way a million times for free - never gets introduced.

Non-determinism makes agents genuinely useful for reasoning tasks. But there’s a symbiotic relationship between the two layers that most teams running agents in production have never explicitly mapped. The teams that do map it spend significantly less per month and scale with far less friction.

At 10 agents running in parallel with no separation between the reasoning layer and the execution layer, the cost compounds linearly with every new task, every new client, and every new environment.

This is one of the first things we look at in an architecture audit.

Follow @madappgang for more on where the line belongs in a production agent stack. by @madappgang

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3 months ago

Watch this if you are building with AI agents.

@trycursor is paying engineers around $1M/year. They do not write code.

If you want to know how engineering teams will operate in 2–3 years, this is worth 9 minutes.

Follow @madappgang , we write about what actually holds up in production. by @madappgang

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3 months ago