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Max Korbacher explains why successful internal development platforms cannot be built on tech alone. He discusses the pitfalls of infrastructure-first thinking, the importance of a clear product mindset, and how to measure real value using DevEx and SPACE metrics. Learn how to align your team, manage tech debt, and foster a thriving community to ensure lasting platform adoption. By Max Körbächer

A SQLite service can keep answering health checks while its WAL grows without a successful checkpoint. One forgotten read transaction is enough to reproduce the risk. Run a controlled drill: Enable WAL mode and create a small table. Open connection A, begin a read transaction, and keep it open. From connection B, commit batches of writes for 60 seconds. Sample WAL bytes and checkpoint results every second. Release A and observe recovery. PRAGMA journal_mode = WAL ; PRAGMA wal_checkpoint ( PASSIVE ); Record the three checkpoint counters plus duration, WAL file size, oldest transaction age, write latency, and free disk bytes. A green SELECT 1 says nothing about checkpoint progress. My fault-injection gate looks like this: Given: one reader holds its transaction When: 10,000 writes commit Then: writes remain bounded And: WAL growth triggers an alert before the disk budget When: the reader closes Then: checkpoint progress resumes and WAL size converges Do not start with an automatic TRUNCATE loop. A busy result is evidence of contention; aggressive checkpoints can add latency without fixing the reader lifecycle. First identify long transactions, ensure result iterators close, and define a maximum transaction age. SQLite’s WAL documentation explains that a checkpoint must stop when it reaches pages beyond an active reader’s end mark. This is why the drill needs concurrency rather than a synthetic file-growth assertion. Operational thresholds should be tied to a disk budget: alert when projected WAL growth reaches the remaining safe window, not at an arbitrary file size. The runbook should name how to find the oldest reader, how to shed writes, and when a restart is safer than waiting. A health check is useful only when it measures the subsystem that is failing.

.The V8 engine speeds up JavaScript by dynamically compiling frequently run bytecode into optimized native machine code. However, if you pass inconsistent argument types to these optimized functions, V8 panics and deoptimizes back to bytecode. Keeping your functions monomorphic (single-typed) prevents this costly deoptimization loop, ensuring maximum runtime execution speed. If you’ve spent as much time digging into V8 execution flags as I have, you quickly realize that JavaScript is constantly rewriting itself under the hood. We like to think of JavaScript as a dynamically typed scripting language. But at runtime, engines like V8 are working tirelessly to turn your code into a highly optimized, statically typed powerhouse. When we violate that type stability, we pay a massive performance tax. How does the V8 engine optimize JavaScript at runtime? V8 uses a multi-tiered compilation pipeline that starts with an interpreter for fast startup times, then upgrades hot functions to optimized machine code using a JIT compiler. By tracking runtime type patterns, the engine can safely make assumptions to skip expensive dynamic lookups. When I look at V8’s execution pipeline, I see two primary systems working in tandem: Ignition (the interpreter) and TurboFan (the JIT compiler). Initially, Ignition compiles your raw JavaScript into bytecode so your app can boot instantly. As this bytecode executes, V8 allocates a data structure called a Feedback Vector for each function. Inside this vector are Feedback Slots (managed by Inline Caches, or ICs). These slots act as recorders, capturing the exact types (or "shapes") of the variables passing through your code. Once a function runs frequently enough to cross an execution threshold, V8 marks it as "hot" and hands it to TurboFan. TurboFan reads those feedback slots, assumes the types will remain identical in the future, and compiles a highly streamlined, native machine code version of that function. What happens when you pass differe

Learning software engineering in the past was a straight forward process, you learn the programming language, you build projects in your portfolio, apply to companies, get a job and life goes on. Doing that in the current times might be a little bit different, as when applying for jobs, you can see some new requirements other than your programming skills and portfolio project such as Prompt Engineering, Work with Agents, Claude Code, and others. You may ask yourself, what are those? And if I am new to Software Engineering, will that change my learning path? Lets discuss all this below. Software Engineering in the Past For a long time, the path into software engineering was clear. You picked a language, maybe Java, Python, or JavaScript. You spent a few months learning the syntax, then the fundamentals: data structures, algorithms, how a database works, how the web sends and receives data. After that, you built things such as A todo app, weather app, clone of a website you liked. These projects went into a portfolio, usually a GitHub profile and a simple personal site. Then you applied to companies, passed a technical interview, and started your first job, so the skills you needed were stable, if you learned React in 2018, React was still useful in 2021. Tools changed, frameworks came and went, but the core idea stayed the same: you write the code, you understand what you wrote, and you fix it when it breaks. When Did AI Start Becoming Something Required The shift did not happen in one day. It came in steps. The first step was autocomplete , around 2021, tools like GitHub Copilot started suggesting the next line of code while you typed. Most developers saw it as a nice helper, nothing more. It saved you from writing boilerplate, but you were still the one thinking. The second step was chat , when ChatGPT and Claude became popular, developers started using them to explain errors, review code, and write small functions. Still a helper, but a much stronger one. At this

Try asking this in your next team meeting: "Who on our team is the best at using AI?" There will probably be a moment of silence. Someone thinks of the person who uses the tools the most. Someone thinks of the person who's good with prompts. Someone thinks of whoever recently produced something impressive-looking with AI. But none of these answers feels convincing. There's a gut sense that something is off — and yet nobody knows what the right criterion would be. Nearly every company is standing in front of this wall right now. AI competence is rising to the top of hiring and performance criteria, and there's no yardstick to measure it with. This essay proposes one. The short answer is a set of five axes I call ABCD2. But before that, we need to fix the question itself. The very idea of "good at using AI" is wrong The title of this essay says "good at AI" — but that phrasing is itself the trap. The word "using" carries a hidden frame: AI is a tool, and the person is a user. It puts AI on the same shelf as Excel and Photoshop — something you get skilled at operating. On top of that frame, evaluation naturally becomes a measure of tool proficiency. How many features do you know? How fluently do you operate it? But AI is not a hammer. AI is an engine that judges and executes. It's less like a tool and more like a junior colleague you can delegate work to. And once you think of a junior colleague, the frame flips. Behind every junior who performs well, there is invariably a senior who makes them perform well — someone who explains the context of the work, lays out the criteria for judgment, makes it safe to ask about ambiguities, and verifies the output. That senior isn't "using" the junior. They are making the junior do the work well . AI is exactly the same. The person who truly gets the most out of AI is not the person who is good at using AI, but the person who helps AI do the work well. And what do they help with? Two things, broadly. One is knowledge building — pr

Jurig (Sundanese: ghost ) — an autonomous AI agent that haunts your binaries. .-. ██ ██ ██ ██████ ██ ██████ (o o) ██ ██ ██ ██ ██ ██ ██ | u | ██ ██ ██ ██████ ██ ██ ███ | | ██ ██ ██ ██ ██ ██ ██ ██ ██ '~-~' █████ ██████ ██ ██ ██ ██████ autonomous reverse-engineering agent · android · binary · frida Point it at an APK, XAPK, or native binary and it plans, decompiles, searches, hooks, captures traffic, and writes you a report — by itself. In one live run it took a real Android loan app, auto-extracted the XAPK, decompiled 13,367 classes , grepped the sources, and surfaced a hardcoded AES key with a zero IV plus the full API endpoint map — then asked me whether it should go dynamic with Frida. This post is the build story: the architecture, the design bets, and the three bugs that genuinely fought back. Repo: https://github.com/ReverserID/JURIG Why build another agent? Existing "AI reverse engineering" is mostly a pile of MCP servers you wire into a chat client. That's fine, but I wanted something opinionated: Autonomous , not chat — it drives a real toolchain end to end. A single portable binary — no Python venv soup, no MCP daemons. Multi-model — my Claude subscription, OpenRouter, local Ollama, Kimi, Qwen. A TUI that feels like a hacker tool , not a log dump. So: Go. Charmbracelet for the TUI (Bubble Tea + Lipgloss + Glamour). And a hard rule — no MCP . Every capability is a native Go function that shells out to a portable RE binary, or does the work in pure Go. Architecture ┌─ agent loop ─┐ plan → ask scope → recon → locate → dynamic → report │ │ │ LLM router │ anthropic · openai-compat (openrouter/ollama/kimi/qwen) · claude-cli │ │ │ 25+ tools │ jadx · apktool · radare2 · ghidra · frida · adb · proxy │ │ + secret_scan · url_extract · manifest · elf/pe_info · search_code │ │ │ TUI │ animated ghost header · code cards · model picker · NET panel └──────────────┘ One wire format, many providers The whole thing speaks the Anthropic Messages protocol internally. A router a

This is the workflow I follow before I use AI agents to implement any feature or bug fix. 🧭 Requirements/Specification ↓ Design/Architecture ↓ AI Code Generation ↓ Human Review ↓ Build & Static Analysis ↓ Testing & Validation ↓ Defect Resolution ↓ Security & Compliance Review ↓ Release ↓ Production Monitoring vs Claude Code ↓ Implements feature ↓ Codex QA Agent ↓ Runs application ↓ Tests happy path ↓ Tests edge cases ↓ Tests error handling ↓ Produces QA report This will resolve the self-review bias, confirmation bias, or AI-to-AI bias. 1️⃣ Understand Before Writing Code Before touching any code, I try to understand what I'm building and why . I usually start by reading: specs/<module>/<TICKET>-<slug>.md plan/<module>/<TICKET>-<slug>.md status.md Then I review the project conventions: specs/CONVENTIONS.md specs/conventions/core-porting.md Finally, I read the existing implementation (entities, services, mappers, etc.) so my changes follow the existing architecture instead of introducing a new style. 💡 Pro-Tip Good code fits into the codebase. Great code looks like it was always there. 2️⃣ Plan the Change Once I understand the requirements, I identify which architectural layers are affected. I always respect the dependency order: Schema / Entities / DAOs ↓ Mappers / DTOs ↓ Service Layer ↓ Application Layer ↓ Controllers I don't jump ahead of dependencies. If a change is complicated or ambiguous, I document the approach before writing code. --- ## 3️⃣ Write the Code While implementing, I follow the repository's rules. Some examples: | Rule | Detail |---|---|---| | DTOs | Generated from `schema.yml` — never handwritten | | Status values | Sourced only from the Core Porting specification | | Traceability | Every ported behavior includes a source citation | Citation formats I use: - `← Source <path>` - `← PS §...` - `← BR-###` Beyond repository rules, I also try to: - ✅ Match existing naming conventions - ✅ Keep comments minimal and meaningful - ✅ Make small, focused chang

Introdução O nome do princípio vem do próprio nome do projeto de pesquisa (que remete a Deméter, deusa grega da agricultura — a metáfora era "cultivar" software que cresce de forma incremental e adaptável, não do princípio de acoplamento em si). O projeto Demeter investigava como reduzir o custo de manutenção de sistemas orientados a objetos observando que boa parte das mudanças de software quebrava código muito distante do ponto onde a mudança real acontecia — um efeito cascata causado por classes que conheciam profundamente a estrutura interna de outras classes. Essa observação foi confirmada empiricamente alguns anos depois: em 1994, Chidamber & Kemerer publicaram as famosas métricas CK ( A Metrics Suite for Object Oriented Design ), nas quais o CBO (Coupling Between Objects) — quão acoplada uma classe é a outras — se tornou um dos preditores mais fortes de defeitos e esforço de manutenção em estudos empíricos posteriores de engenharia de software. Ou seja: a intuição por trás da Law of Demeter (menos acoplamento = menos bugs ao mudar código) tem respaldo em dados de décadas de pesquisa empírica em qualidade de software. Definição Também chamada de "Principle of Least Knowledge" , a formulação clássica é: Um método M de um objeto O só deve chamar métodos de: O próprio O Os parâmetros recebidos por M Qualquer objeto que M crie/instancie internamente Os componentes diretos de O (seus atributos/campos) Variáveis globais acessíveis a O Resumo popular: "use apenas um ponto" — evite código como: pedido . getCliente (). getEndereco (). getCidade (). getNome () Isso é conhecido como "train wreck" (trem de vagões) — cada . é um vagão acoplado ao anterior. Se a estrutura interna de Cliente ou Endereco mudar, todo código que fez essa travessia quebra, mesmo estando em um módulo completamente não relacionado. Porque isso importa na prática? Quando o método M faz objeto.getX().getY().metodo() , ele passa a depender da estrutura interna de X e Y , não só da interface pública d

Every engineer eventually hits this phase: “My design looks okay… but something feels off.” No compile errors. No obvious bugs. But still: responsibilities feel scattered services feel too big entities feel too thin logic feels duplicated boundaries feel unclear This is normal. Because domain modeling is not about getting it right in one attempt. It is about refining structure until the business behavior becomes clear. Step 1 — Start With the Symptom, Not the Code If your design feels wrong, don’t immediately rewrite everything. First identify the symptom: Common symptoms: too many “Manager” services logic repeated in multiple places unclear ownership of rules too many dependencies between modules frequent “if-else explosion” Each symptom points to a specific modeling issue. Step 2 — Check If Invariants Are Scattered Ask: “Where are my business rules living?” Bad sign: Rules inside services + controllers + helpers This leads to: inconsistent behavior duplicated validation broken business guarantees Good design: invariants live close to the entity or aggregate root Step 3 — Check Entity vs Service Confusion A very common issue: Entities become dumb: only fields no behavior Services become overloaded: all logic all rules all decisions This creates: Anemic Domain Model + Fat Services Fix mindset: Entity = owns behavior + protects state Service = coordinates workflows Step 4 — Check Your Aggregate Boundaries Ask: “What must stay consistent together?” If your answer is unclear, you likely have: wrong aggregates or missing aggregates Example problem: Cart and Order sharing logic This causes: inconsistent pricing unclear lifecycle ownership Fix: Cart = intent Order = truth Step 5 — Look for “Hidden Coupling” Hidden coupling happens when: one module depends on internal state of another multiple services modify same data business rules are duplicated across boundaries This leads to fragile systems. Strong design ensures: each domain owns its own truth. Step 6 — Validate Stat

Every team doc tool promises "search everything." Ours did too — and it still didn't answer the question new hires actually ask: not "where is this doc," but "why does this decision look the way it does, and what else does it touch?" We spent the last few months trying to solve that by treating team docs less like a filing cabinet and more like a graph. Here's what we tried, what broke, and what we'd do differently.

Before a young Athenian took his full place in the city, he spent two years in the ephebeia. Training happened in the gymnasion, organized by tribe, the same tribes that would later send him to represent them in the Boule itself. Nobody handed him full standing first and hoped the judgment would follow. This should have been post seven. It’s showing up as sixteen because the gap only became visible once the room was real enough to test against. The Agora described what a Spec Session does. It never asked whether everyone walking into that room shares an accurate picture of what the agent can actually do. Most rooms don’t. Someone watched a demo and thinks the agent can do anything. Someone else got burned by a bad output three weeks ago and doesn’t trust it with anything real. Nobody’s intuition has been tested against the same tasks, and the spec that comes out of that room ends up too ambitious or too conservative depending on whose untested belief happened to speak first. That gap has a name in Athens. The gymnasion existed because nobody was handed a place in the city first and expected to develop judgment on the job. The ephebeia ran two years, training built around a specific fact. Physical readiness and civic judgment weren’t taught in separate places. They happened in the same space, under the same supervisors, organized by the same tribal groupings that would later structure how the city actually governed itself. The same word, gymnasion, ended up naming both the training ground for eighteen-year-olds and the buildings where Plato and Aristotle did their most serious thinking. Academy and Lyceum were gymnasia first. Nobody separated the trial from the reflection. The trial was how the reflection got earned. That’s the part worth taking seriously. Testing a tool and understanding a tool were never two different activities. The testing is how the understanding gets built. A team that reads documentation about what an agent can do has a description. A team tha

İlk insanlı katı-hal uçuşu manşetlere düştüğünde çoğu haber "yeni motor" diye başladı. Oysa bir mühendisin gözünde olay motorda değil. Elektrik motoru bu denklemde en çözülmüş parça: hafif, sessiz, yüksek verimli. Asıl kararların döndüğü yer, o motoru besleyen enerjinin nasıl depolandığı. Bu yazı, duvarın neden onlarca yıl boyunca hep aynı yerde — bataryada — durduğunu sistem mühendisliği açısından ele alıyor. Sistem kısıtı: enerjiyi değil, ağırlığı taşırsınız Bir uçağı tasarlarken bütçeniz enerji değil, kütledir. Depoladığınız her watt-saat bir ağırlıkla gelir ve bu ağırlık kaldırma kuvvetiyle ödenmek zorundadır. Ölçüt enerji yoğunluğu , yani Wh/kg. Kerosen bu metrikte devasa bir avantaja sahip olduğu için jetler onlarca yıl tartışmasız kazandı. Elektrikli havacılığın tüm hikâyesi, bu tek sayıyı yukarı çekme mücadelesidir. Neden hep batarya? Kısır döngünün matematiği Menzili artırmak için daha fazla batarya eklersiniz. Ama batarya ağırdır; eklediğiniz kütle daha fazla kaldırma, dolayısıyla daha fazla enerji ister. O ek enerji için yine batarya eklersiniz ve döngü kendini besler. Buna ağırlığın kısır döngüsü denir ve bir sistem sınırıdır: hücrenin Wh/kg değeri belli bir eşiği aşmadıkça, motoru ne kadar iyileştirirseniz iyileştirin duvar yerinde durur. Kararın kaldıraç noktası bu yüzden hücre kimyasıdır. Kimyayı değiştiren tasarım kararı Bugünün lityum-iyon hücreleri pratikte ~260 Wh/kg dolayında bir tavana oturur. Bu tavanın altında yatan iki bileşen sıvı elektrolit ve grafit anottur. Katı-hal yaklaşımı burada iki radikal karar verir: sıvı elektroliti katı bir iletkenle değiştirir ve anodu lityum-metale taşır. Sonuç ~410 Wh/kg, yani mevcut tavanın %60'tan fazla üstü. Yan kazanım da en az ana kazanım kadar önemli: yanıcı sıvı ortadan kalkınca termal kaçış riski düşer, hücre daha kararlı olur ve 15 dakikanın altında %80 şarj operasyonel olarak mümkün hâle gelir. Bu üç sayı tek başına değil, birlikte anlam kazanır. Havacılıkta bir aracın yerde geçirdiği süre doğrudan m

Suspect-condition workflows are deceptively simple to prototype and surprisingly hard to make defensible . Anyone can flag "this member might have HCC X." Building a system whose output survives a RADV audit is a different problem. This is a walkthrough of the three stages and the engineering decisions that matter at each. Stage 1: Identify Identification is pattern detection over a member's clinical record — labs, medications, prior diagnoses, utilization. Model it as a set of rules or features that emit candidate HCCs: def identify_suspects ( member ): suspects = [] if member [ " labs " ]. get ( " a1c " , 0 ) >= 9.0 and " insulin " in member [ " meds " ]: suspects . append ({ " hcc " : " HCC38 " , " trigger " : " a1c>=9 + insulin " }) if member . get ( " egfr " ) and member [ " egfr " ] < 30 : suspects . append ({ " hcc " : " HCC326 " , " trigger " : " egfr<30 " }) return suspects The temptation is to maximize recall here — flag everything. Resist it. Every unvalidated suspect you generate is downstream work and downstream risk. Stage 2: Validate (the stage that actually matters) Validation attaches evidence to each suspect and scores its defensibility. This is the difference between a documentation opportunity and an audit liability. def validate ( suspect , member ): evidence = collect_evidence ( suspect [ " hcc " ], member ) # labs, rx, prior dx suspect [ " evidence " ] = evidence suspect [ " confidence " ] = score_evidence ( evidence ) suspect [ " defensible " ] = suspect [ " confidence " ] >= 0.7 return suspect Key design rule: a suspect with an empty evidence array should never reach a coder. Make that a hard gate, not a soft warning. Under CMS-HCC V28 and current audit posture, a captured-but-unsupported diagnosis can be extrapolated across a contract into a real clawback — so "defensible by default" is the right engineering stance. Stage 3: Capture Capture routes validated suspects to the right human with the evidence inline, so the clinician or coder can

Every developer who has worked with LLMs has been there. You ask the model for JSON. You describe the schema. You say "please only respond with valid JSON." And sometimes, it still breaks. Your application crashes because the model returned a string where you expected an integer. Or it wrapped the JSON in markdown code blocks. Or it omitted a required field. Spring AI 2.0 has a solution that treats this like a real engineering problem instead of a prayer. The Problem When you use structured output in Spring AI, the workflow goes like this: You define a Java type (a record, class, or enum) Spring AI generates a JSON schema from that type The schema gets appended to the prompt sent to the LLM The model returns a response Spring AI attempts to deserialize the response into your type This works well with frontier models like Claude and GPT-4. But smaller open-source models, like Llama 3.2 1B running locally via Ollama, fail more often. They might return null for a primitive field, omit required fields, or produce malformed JSON. When it fails, you get a deserialization exception. Your endpoint returns a 500 error. Spring AI provides no built-in recovery mechanism. The Old Approach: Hope Consider a conference talk submission system. Speakers submit messy, unstructured abstracts. You want to extract structured data: public record TalkSubmission ( String title , String abstractText , Level level , // BEGINNER, INTERMEDIATE, ADVANCED Track track , int duration , List < String > tags , String speakerHandle ) {} Here is what the basic typed response looks like: @PostMapping ( "/typed" ) public TalkSubmission typed ( @RequestBody String rawSubmission ) { return chatClient . prompt () . system ( systemPrompt ) . user ( spec -> spec . text ( "Extract the talk submission: {submission}" ) . param ( "submission" , rawSubmission )) . call () . entity ( TalkSubmission . class ); } You define your type. Spring AI generates the schema and appends it to the prompt. The model gets the in

The "Obvious" Math That's Wrong Engineer A: "Datadog is $15K/month. Prometheus is free. We should self-host." Engineer B: "But we'd need to pay an SRE to run it. That's $150K/year." Engineer A: "Prometheus doesn't need a full SRE. It's easy." Engineer B: "Famous last words." This conversation happens at every company. Both sides have points. The real math is more complex. The Total Cost Breakdown Managed (Datadog, New Relic, Dynatrace) : Licensing: $X/month (scales with hosts, events, logs) Integration time: 1-2 weeks per service Training: 1 day per new hire Ongoing: minimal Self-hosted (Prometheus + Grafana + Loki + Alertmanager) : Infrastructure: hosting costs (~$500-$5000/month depending on scale) Initial setup: 2-4 weeks of engineering time Ongoing maintenance: 10-20% of 1 FTE Upgrade costs: quarterly, each upgrade ~1 week Storage growth: ~20% per year Expertise: junior → senior SRE hire required The honest answer: managed is cheaper for teams under 50 engineers. Self-hosted becomes cheaper around 200+ engineers if you can run it well . The Real Variables It's not just licensing cost vs. hosting cost. These factors matter more: 1. Data volume growth Managed tools charge per GB ingested or per metric. If your logs 10x, your bill 10x's. Self-hosted scales linearly with compute. You control the growth. 2. Retention requirements Managed tools often charge extra for long retention. Self-hosted you store as much as your disk allows. 3. Cardinality Prometheus dies at high cardinality. Datadog handles it but charges more. High-cardinality metrics are where self-hosted breaks. 4. Incident rate Heavy incident load means heavy query load on your monitoring tools. Self-hosted needs bigger compute for this. 5. Team expertise If your team has never run Prometheus, you'll spend 6 months in the pit learning cardinality mistakes, retention tuning, and HA setups. That's not free. The Break-Even Calculation Rough calculation for a 50-engineer startup: Managed (Datadog) : - Licensi

The most expensive thing you can do with an AI agent is watch it. Not audit it. Not review its output. Watch it -- step by step, approval by approval, second-guessing every action before it takes the next one. And yet that is precisely how most engineering teams are deploying AI agents in 2026: on a leash so short the agent cannot take three steps without a human tapping it on the shoulder. I understand why. The models hallucinate. The stakes are real. Nobody wants to be the engineering manager who let an AI agent push a bad migration to production at 2am. So we wrap the agents in confirmation dialogs, require human sign-off at every branch point, and celebrate our careful governance. What we have actually built is an automation system that requires more human attention than the manual process it replaced. The better answer is not more control at the action level. It is better design at the intent level. Steer by intent, monitor by exception. Tell the agent clearly what outcome you need, what it must never do, and what constitutes a result worth stopping for. Then let it work. Watch the outcomes, not the steps. We have built automation systems that require more human attention than the manual process they replaced. That is not a governance success. That is a design failure. Why we got here The model for human-AI collaboration that most teams are using today was inherited from the model for junior developer supervision. You review every pull request. You approve every deployment. You sign off on every schema change. That model exists because junior developers are learning, because their mental models are incomplete, because their judgment has not yet been earned. Applied to AI agents, it assumes the same thing: the agent is a novice that needs supervision. But an AI agent is not a junior developer. It does not have an incomplete mental model of the codebase that will improve with mentorship. It has exactly the mental model you gave it via its context, its tools, and

The cost of writing code dropped; the cost of owning it didn't. A framework for deciding which changes are actually cheap in the AI era. The post The cost of saying yes has changed appeared first on The GitHub Blog .

Everyone recommends ByteByteGo and The Pragmatic Engineer. Don't get me wrong, they're great... but the best engineering writing of the last two years is coming from newer publications nobody's put on a list yet. Here's what survived my filter. I have a rule: if I haven't opened a newsletter in three weeks, I unsubscribe. No guilt, no "maybe later" folder. It's the only way to keep email useful when every engineering team, indie hacker, and AI startup on the planet is running a Substack. That rule has consequences. Over the past couple of years it has killed off almost every famous-name newsletter in my inbox — not because they got worse, but because they got comfortable. Meanwhile, a new generation of engineering publications launched around 2023–2024 started earning their slot every single week. They're smaller, sharper, and written by people still close to the work. The other thing my rule revealed: AI engineering quietly became its own discipline. Not "AI news" — there are a thousand newsletters rehashing model launches. I mean the craft of building production systems on top of LLMs: agents, evals, brownfield integration, governance, cost. That coverage barely existed two years ago. Now it's the most valuable section of my inbox, which is why it leads this list. So here's what survived. Twenty-six newsletters, organized by topic, heavy on publications you haven't seen on every listicle. Steal the whole list. 🤖 AI Engineering & Production AI Two years ago this category didn't exist. Today it's the most important one here, because building with LLMs in production is genuinely different work — different failure modes, different economics, different skills — and general engineering newsletters mostly aren't covering it. Latent Space — swyx & Alessio Fanelli. swyx literally coined "AI engineering" as a discipline, and this is its watering hole: podcast, essays, and the AINews digest covering frontier models, agents, and the career path itself. The anchor of the categ

I've been tuning Codenames AI , a small web game where an LLM plays Codenames with you. Clue generation is tightly constrained: one word, a count, optional intended targets, JSON on the wire, then deterministic validation before anything reaches the board. As the project started attracting regular players, I wanted to improve the gameplay experience without blowing out costs. Moving one model generation from gpt-4o-mini to gpt-5-mini was my first instinct. The default reasoning setting made responses an order of magnitude slower for this workload. Minimal reasoning looked like the obvious compromise: newer model, responsive gameplay. I expected to compare clue quality, latency, and cost while the surrounding prompt, validator, and consumer contracts stayed put. That last part was wrong. The experiment stopped behaving like an A/B test What showed up was structural, and it showed up in places that had been stable for months. Validation failures started rising. Retries started rising. Entire candidate batches started failing before the game ever saw a clue. The sharpest signal came from a clue-selection path that had run untouched for months, and it hard-failed for the first time. They weren't latency regressions so much as architectural ones. It is easy to read that as "minimal reasoning made the model worse." More often, the failures were exposing gaps in contracts that had looked fine under the previous model. What each failure actually invalidated Eventually every failure traced back to one of three layers: Prompt contracts ask for exactly count targets and, in batch mode, several distinct candidates. Deterministic validators reject target/count mismatches and filter invalid candidates before anything downstream runs. Downstream consumers only see survivors. Empty batches retry with rejection feedback, then fall back if needed. Those layers share one job: enforce the same invariants. The failures below cut across all three rather than mapping one to one. Side comm

Ben O'Mahony discusses building custom AI-powered Language Server Protocols (LSPs) that go beyond standard rule-based checkers. He explains how to instrument AI agents natively with OpenTelemetry to track concrete user actions (accepting, dismissing, or regenerating code fixes) as implicit labels, creating a continuous data flywheel to distill frontier capabilities into cheaper, local SLMs. By Ben O'Mahony