Thursday, May 21, 2026

An obligatory post about vibe coding

(An obligatory post about vibe coding - everyone needs to make at least one, they say.)

I've been using my $20 Claude subscription to tinker on some side projects in my personal time.

It's astonishing how much can be done with the basic plan, which makes me wonder how much it will actually cost once the VC subsidies end.

I've also gained quite a bit of insight without going neck deep into multi-agent, spec driven, ralph loop, <insert latest buzzword> and such bleeding edge techniques or tools. 

I've stuck to a simple workflow 

  1. It involves Claude code on my Macbook, an architecture.md, a backlog.md and a simple claude.md. I also get the AI to create a more detailed but transient plan-<complex-feature>.md for complex, work in progress features
  2. I use Claude in the browser for learning, research and brainstorming

I've iterated on my personal workflow over multiple projects. It's been "productive", meaning I've created many experimental projects, including some that may have legs. Some have been purely personal and custom productivity type of tools. I've not really read the code it has produced because all of it was done while I was watching TV or doing chores. Some even in programming languages I have no prior experience with.

Learnings (using Claude as of May 2026):

  • Despite having Claude.md and tests it can make subtle mistakes. On occasion
    • It leaves docs in an inconsistent state and requires a reminder/instruction to find and fix them
    • It forgets to read some of the instructions in Claude.md
    • It writes docs in incorrect folders ignoring the designated docs/ folder
    • It accidentally deletes features as part of larger changes, esp if those changes cannot be tested (UI)
    • It freezes
    • (My work related/professional experience is different which I won't speak about here)
  • Judgement and Taste absolutely matter
    • Identifying problems worthy of solving, brainstorming potential solutions and implementing them in the correct order is really where a good human engineer does really well
    • Producing a usable application that is based on the most popular frameworks, on which the AI is trained on is a shockingly easy and delightful experience. Can't say the same about juggling multiple agents and reviewing lots of AI generated PRs from multiple team members though 

Here are some of my older experiments: https://github.com/AshwinJay/ai-experiments

Here is one that I may actually use one day: https://github.com/AshwinJay/project-tracker 

Until next time. 

Tuesday, May 19, 2026

A poster: The architecture of high-performing teams

I had a bit of free time to introspect on how I work with my teams. I summarized the things I've learned to pay attention to while supporting them. None of this is novel or earth shattering as I do read a bit. I may have absorbed and synthesized information from various sources and certainly from personal experience. In fact it may be hard to make it simpler. I gave Claude some of my hand written notes and asked it to create a poster.

So here it is:


Here's how the relationships are mapped: 

Top-down causality - the vertical flow shows that foundation shapes culture, culture shapes people, people form teams, teams execute projects, and projects produce outcomes. Each layer depends on the one above.

Horizontal duality - Vision/Mission/Charter and Culture/Values each exist at both company and team level, shown as parallel halves with a bidirectional connector. The two must align.

Bias as a structural outlier - it gets a dashed coral border rather than a solid one, signalling it's something to monitor rather than build on.

The tactics matrix - a 2D grid that makes the Start/More/Less/Stop × Fast/Medium/Slow intersection explicit. Every tactic can be applied at any pace.

The 5 Cs as a causal chain - not just a list but a sequence: Clarity + Context to deliver Content, the right Content hits the target, and Consistent delivery builds Confidence. Also Confidence that the Content has value to the Customer, and inspires Confidence in the Team/Product/Company.

Process at the bottom rather than the side signals it's the substrate - it doesn't direct the other layers, it captures and feeds them.

One thing that I have left out is the feedback loop that should exist in any healthy organization of any size. I left it out because it would make the poster busy and also serves as a good test to see if people pick up on that missing piece. Hopefully it sparks some interesting conversations about what the people in the organization at any level think is expected of them - Just follow instructions or act like an owner and help shape the culture, adapt and grow.

Down below, is another way of looking at the same framework but at different levels/scope: 

Misalignment becomes visible. If a team's norms don't match the company's stated culture, or someone's personal values sit at odds with the team's, you can see exactly where the friction is coming from.

Tactics look different at each level. Start/More/Less/Stop is often thought of as a personal coaching tool, but at org level it's essentially a strategic portfolio decision, and at team level it's about changing practices. The same tool, three different conversations.

The 5 Cs are a diagnosis at every scope. Weak clarity in a team usually traces back to weak clarity at org level - if the company's narrative is inconsistent, the team can't be confident, which rolls down to individuals.

Process is the only row where org and individual rarely align. Company-wide systems almost never match how individuals actually work - which is often where friction and shadow processes come from.

Until next time.

Wednesday, May 06, 2026

Spring reading - 2026

Some reading for Spring (As usual, a hat tip to Hacker News, Reddit, Youtube, Spotify, Twitter, Bluesky and my other feeds).

Tag(s)                      Link
aiAI CEO vs Engineer (2026). - YouTube
aiAI Didn't Break the Senior Engineer Pipeline. It Showed That One Never Existed.
aiAMD Senior AI Director confirms Claude has been nerfed
aiAnnouncing Fin Apex - our custom LLM powering Fin
aiAnthropic's Claude Mythos isn't a sentient super-hacker, it's a sales pitch — claims of 'thousands' of severe zero-days rely on just 198 manual reviews | Tom's Hardware
aiAre AI agents actually slowing us down? - by Gergely Orosz
aiBenedict Evans: OpenAI’s Moat Problem & the Future of Software - YouTube
aiBreaking down Jina v5-text: Small models with outsized performance - YouTube
aiCheap Chinese models are overtaking Anthropic • The Register
aiCognitive Debt: When Velocity Exceeds Comprehension | rockoder
aiData vs Hype: How Orgs Actually Win with AI - The Pragmatic Summit - YouTube
aiDylan Patel (SemiAnalysis): The Datacenter in 2026: CPUs, RL Environments & Agent-Driven Workloads - YouTube
aiFlow generation through natural language: An agentic modeling approach (2026) - Shopify
aiInference Engineering with Baseten's Philip Kiely - YouTube
aiJensen Huang actually said it! - YouTube
aiMy AI Adoption Journey – Mitchell Hashimoto
aiNVIDIA B300 Blackwell Ultra: A Technical Deep Dive - YouTube
aiOn genAI: Was prototyping really a bottleneck? | Frank Elavsky
aiRamaLama: Making working with AI Models Boring by Cedric Clyburn - YouTube
aiShipping faster, thinking less? The AI code verification trap - LeadDev
aiSlop Creep: The Great Enshittification of Software | Boris Tane
aiSome Things Just Take Time | Armin Ronacher's Thoughts and Writings
aiSpec-Driven Development with AI Agents From High-Level Requirements to Working SW by Anton Arhipov - YouTube
aiThe AI Great Leap Forward
aiThe Grid Said No
aiThe hidden danger of shipping fast - by Cleo
aiThe peril of laziness lost | The Observation Deck
aiVibe coding failure proof AI agents with open source Code Puppy & DBOS - YouTube
aiWe gave Claude Access to All Python Variables - YouTube
aiWhat happens to AI reasoning quality when you compress a model? We tested it! - YouTube
aiWhy AI Progress Will Stall | Data Processing Club
aiWhy Fully Self-Driving Cars Are Almost Impossible | The Limit - YouTube
ai,funDeath by Clawd | SaaSpocalypse Survival Scanner
ai,javaA Coding Agent in 260 Lines of Java - @maxandersen
ai,javaA Simple Coding Agent in a Loop with LangChain4j, Jbang, and Gemini
ai,javaAI4JVM — Java & JVM AI Ecosystem Guide: Agent Frameworks, Inference Engines & Tools
ai,javaAgentic AI Patterns by Kevin Dubois - YouTube
ai,javaEasy Agent Skills with Spring AI and the new Skillsjars project! - YouTube
ai,javaEnabling AI Agents to Use a Real Debugger Instead of Logging
ai,javaHashSmith, Part 3: I Automated My Way to a 27% Faster Hash Table | Bluue Whale
ai,javaProduction-Ready GenAI with Open Models for Java Teams - YouTube
ai,relnoteTransformersjs v4 - YouTube
ai,security310 | Breaking Analysis | RSAC 2026 preview: AI hype meets operating model reality - YouTube
ai,securityWhy AI Infrastructure is Harder to Secure Than Cloud - YouTube
ai,systemP99 CONF 2025 | KV Caching Strategies for Latency-Critical LLM Applications by John Thomson - YouTube
ai,systemZero-Copy GPU Inference from WebAssembly on Apple Silicon
dataBehind the hype: managing billion-scale embeddings in Elasticsearch and OpenSearch by Pietro Mele - YouTube
dataData Lakehouses Were Never This Simple Until DuckLake - YouTube
dataDuckLake v1.0: The Lakehouse Format Built on SQL Reaches Production-Readiness – DuckLake
dataExploring Table Formats - Iceberg & SlateDB
dataExploring the Iceberg Ecosystem with DuckDB-Iceberg – DuckDB
dataFloe: A SQL Compute Service for the Data Lakehouse (Kurt Westerfeld + Mark Cusack) - YouTube
dataJSON and Tell: The Variant Type in Parquet and Iceberg - YouTube
dataKeeping a Postgres queue healthy — PlanetScale
dataScaling Near-Real-Time Iceberg Writes In Go - YouTube
dataScaling PostgreSQL to power 800 million ChatGPT users | OpenAI
dataTinkerPop Wide: Exploring Graph Explorer - YouTube
dataTurns out we didn’t need that second index | how Feldera's SQL Compiler Eliminates Duplicate Indexes in Incremental Joins
dataUnderstanding Apache Lucene - More than just full-text search - Speaker Deck
dataVector search using only Parquet and DataFusion – Xiangpeng’s blog
datamarimo added a BUNCH of new features - YouTube
data,relnoteAnnouncing DuckDB 1.5.0 – DuckDB
data,systemClickHouse's C++ and Rust Journey - YouTube
data,systemGPU accelerated data processing on Velox and Presto - Zoltan Arnold Nagy, IBM - YouTube
funShipping a button in 2026… - YouTube
golang,systemWe Rewrote JSONata with AI in a Day, Saved $500K/Year | Reco
java10 or more reasons to not use JDK’s HttpClient • Brice Dutheil
javaAlgebraic Types: The Math Hiding in Your Java Code — fbounded
javaCatching the 137-Killer: A Java Memory Forensics Investigation by Martijn Dashorst - YouTube
javaEVERYTHING new in Java 26, explained! - YouTube
javaGraalVM 25: What's New and What's Next by Alina Yurenko - YouTube
javaHTTP/3 in Java 26 - Inside Java Podcast 48 - YouTube
javaHardwood: A New Parser for Apache Parquet - Gunnar Morling
javaHow we integrated Project Leyden into Quarkus - Quarkus
javaInside trivago’s GraalVM Migration: Native Image for GraphQL at Scale
javaJava 26: MemoryMXBean.getTotalGcCpuTime() — Explicit GC Cost | Jonas Norlinder
javaJava Next - Live Stream from JavaOne - YouTube
javaJava Performance Update: From JDK 21 to JDK 25 - YouTube
javaJava for an AI World - JavaOne Keynote - YouTube
javaManaging Native Memory in Java: Arenas, Malloc, and Custom Pools
javaOptimizing Recommendation Systems with JDK’s Vector API | by Netflix Technology Blog | Mar, 2026 | Netflix TechBlog
javaP99 CONF 2025 | A Java Developer's Quest for I/O Performance by David Vlijmincx - YouTube
javaP99 CONF 2025 | ZGC: A Decade of Innovation by Stefan Johansson - YouTube
javaPerformance Tuning Java Apps for Kubernetes: From Startup Time To Conta... Ryan Jarvinen & Daniel Oh - YouTube
javaPilot 0.1.0: An Interactive TUI for Maven | gnodet's blog
javaQuarkus Insights #239: Reactive Loom - YouTube
javaQuarkus Insights #240: Quarkus & Gizmo 2 - YouTube
javaQuarkus Insights #245: HTTP/3 support coming in Vert.x 5.1 - YouTube
javaThe Arrival of Java 26 | java
javaThread Safe Native Memory in Java
javaUnboxing Java 26 for Developers - Inside Java Newscast #108 - YouTube
java,relnoteHelidon 4.4.0 Release: LTS Support via Java Verified Portfolio (JVP), Agentic LangChain4j, OpenTelemetry Metrics & Logs, and Helidon JSON | Helidon
java,systemHigh-Speed Crypto Trading: JVM Techniques Behind Bitvavo’s µs Revolution - Oleg Lobanov, Marcos Maia - YouTube
java,wasmWebAssembly and the Future of the JVM Ecosystem by Andrea Peruffo @ Spring I/O 2026 - YouTube
rustInterpreting near native speeds with CEL and Rust | howardjohn's blog
rust,systemjsongrep is faster than {jq, jmespath, jsonpath-rust, jql}
systemDatabase Connection Pool Sizing - Demystified! by Jasmin Fluri - YouTube
systemDevnexus 2026 - The Golden Path Starts at Home Engineering Developer Experience from Laptop to Prod - YouTube
systemHow AWS S3 is built - YouTube
systemHow C++ Finally Beats Rust at JSON Serialization - Daniel Lemire & Francisco Geiman Thiesen - YouTube
systemHow to build a distributed queue in a single JSON file on object storage
systemLightning Talk: Production-Grade AI Isolation: K0s + Kata Containers for Zero-Tru... Prashant Ramhit - YouTube
systemOpenData Timeseries: Prometheus-compatible metrics on object storage | OpenData
systemP99 CONF 2025 | Building a High-Performance CI Cloud from the Ground Up by Aditya Maru - YouTube
systemP99 CONF 2025 | Mechanical Sympathy in Cooperative Multitasking by Kenny Chamberlin - YouTube
systemP99 CONF 2025 | Rethinking Durable Workflows and Queues: A Library-based Approach by Qian Li - YouTube
systemP99 CONF 2025 | Timeseries Storage at Ludicrous Speed by Duarte Nunes - YouTube
systemP99 CONF 2025 | Translations at Scale: Memory Optimization Techniques by Cristian Velazquez - YouTube
systemRunning Wasmtime in Hardware-Isolated Microenvironments - Danilo (Dan) Chiarlone, Microsoft - YouTube
systemTIL: macOS’ Hidden Gem – The "caffeinate" Command! - James Brooks
systemThoughts on the Bluesky public incident write-up – Surfing Complexity
systemturbopuffer: Object Storage-native Database for Search (Simon Eskildsen) - YouTube
wasmGraalVM: WebAssembly for the JVM Ecosystem - Fabio Niephaus & Shaun Smith, Oracle - YouTube
wasmThe State of Zero-Dependency Wasm: A 2026 Update from Wazero and Chicory @ Wasm I/O 2026 - YouTube

Until next time!