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This repo from GitHub just hit 119k stars 🔥 it solves the problem every team hits when they move from one AI-assisted fi...
10/08/2026

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

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Microsoft laid off 13,000 engineers over the last 12 months while simultaneously increasing spend on AI and cloud infras...
06/08/2026

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 MAG | Software Development Company for the architecture patterns we run across 70+ engineers and 100+ production projects.

We tested three LLM expanders on 860 real code search queries. All three made results worse. Here is exactly why…Bigger ...
30/07/2026

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.

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28/07/2026

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 Claude by Anthropic across 70+ engineers and 100+ production projects. What these engineers describe maps directly to what holds up under real client pressure.

Follow MAG | Software Development Company for more production signal on agentic development.

Something spent $400 in tokens last Tuesday. Which agent did it, and what exactly was it doing?Agent frameworks ship you...
21/07/2026

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 ex*****on traces and evals, and which ones leave you reaching for Langfuse as a bolt-on.

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Why do so many CEOs come from engineering backgrounds?Jeff Bezos studied computer science. Satya Nadella started as an e...
15/07/2026

Why do so many CEOs come from engineering backgrounds?

Jeff Bezos studied computer science.
Satya Nadella started as an engineer.
Jensen Huang has a degree in electrical engineering.

In AI it looks the same: Dario Amodei, Alexandr Wang , 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 ex*****on 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 ex*****on is too small to have a translation layer in between.

At MAG | Software Development Company , engineering has been at the top since day one.

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

The decisions that do not matter at one agent become load-bearing at three, critical at ten, and existential at fifty.Me...
09/07/2026

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 ex*****on 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.

A senior engineer on Reddit with 10 years of experience just explained the one skill he’d teach every vibe coder.It quie...
07/07/2026

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 ex*****on 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 MAG | Software Development Company for more on where the line belongs in a production agent stack.

01/07/2026

Watch this if you are building with AI agents.

Cursor 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 MAG | Software Development Company , we write about what actually holds up in production.

Two AnthropicIA engineers gave a 16-minute talk that will teach you more about agent architecture than most $1000 course...
25/06/2026

Two AnthropicIA engineers gave a 16-minute talk that will teach you more about agent architecture than most $1000 courses:

Barry Zhang and Mahesh Murag built Claude by Anthropic Skills — Anthropic’s framework for giving agents reusable domain expertise. In this talk they lay out the architecture behind it and the reasoning most tutorials skip.

What they cover:

→ How to make agent systems modular instead of prompt spaghetti
→ The split between MCP (access) and Skills (expertise) — and why that distinction matters
→ How Fortune 100 companies encode internal workflows as Skills
→ What a production-ready agent stack actually looks like

By the end you have a clear pattern for building systems that reuse expertise instead of re-solving tasks every time.

If you’re building AI agents, this is 100% worth watching. It’ll probably change how you structure things.

Link to the video: https://lnkd.in/dxnG2CgU

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