Saturday, August 01, 2026

Summer reading - 2026

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

Tag(s)                      Link
aiAgent orchestration, simplified - YouTube
aiBenchmarking Coding Agents on Databricks’ Multi-Million Line Codebase | Databricks Blog
aiBeyond local tools: Deep dive into MCP with Spring AI by James Ward / Maximilian Schellhorn - YouTube
aiBurnout Will Go Up, and We’re Doing It to Ourselves with Nathen Harvey - YouTube
aiContributor Poker and Zig's AI Ban | Loris Cro's Blog
aiHarness Engineering is not Enough: Why Software Factories Fail — Dex Horthy, HumanLayer - YouTube
aiHow does Claude Code *actually* work? - YouTube
aiHow to Automate AI Evals (Correctly) - YouTube
aiHow to Prepare for Coding Interviews (with HackerRank) - YouTube
aiI Hated Every Coding Agent, So I Built My Own — Mario Zechner (Pi) - YouTube
aiNothing has changed about software engineering | Ben Eggers | Bug Bash 2026 - YouTube
aiSkill Distillation | Tomasz Tunguz
aiThe layoffs will continue till we learn to use AI
aiUber: Leading engineering through an agentic shift - The Pragmatic Summit - YouTube
aiUsing LLM in the shebang line of a script | Simon Willison’s TILs
aiWeek 2: Interning at a YC startup
aiWhat you need to learn from claw-code repo
ai,javaDevnexus 2026 - Keynote - Its Up To Java Developers to Fix Enterprise AI - Rod Johnson - YouTube
data30x faster than Prometheus: How we rebuilt Elasticsearch as a leading columnar metrics datastore - Elasticsearch Labs
dataApache Fluss: A Streaming Storage for Real-Time Lakehouse (Jark Wu) - YouTube
dataApache Gluten at Microsoft William Chen, Microsoft - YouTube
dataDataFusion + DuckLake — Zac Farrell | Data Debug SF - YouTube
dataDemocratizing Machine Learning at Netflix: Building the Model Lifecycle Graph | by Netflix Technology Blog | May, 2026 | Netflix TechBlog
dataDuckDB-Quack Announcement at AI Council 2026 - YouTube
dataFrom Storage Formats to Open Governance: The Evolution to Apache Polaris (Prashant Singh) - YouTube
dataHartmut Armbruster – What If We've Been Scaling Stream Processing Wrong All Along #bbuzz - YouTube
dataMatthias Niehoff – DuckDB beyond the notebook #bbuzz - YouTube
dataOpen Source Friday with Gunnar Morling with Hardwood - YouTube
dataScott Haines: Unifying Open Lakehouse Governance via Policy Portability, Seattle/Bellevue April 2026 - YouTube
dataThe Deconstructed Database at Datadog - YouTube
dataWriting Custom Table Providers in Apache DataFusion - Apache DataFusion Blog
data​Performant Interactive Queries | Jayant Shrivastava, Gene Bordegaray, Justin O'Dwyer, Datadog - YouTube
data,javaAnkit Jain – From Inverted Index to Columnar Vectorized Execution Search #bbuzz - YouTube
data,javaHardwood 1.0: A Fast, Lightweight Apache Parquet Reader for the JVM - Gunnar Morling
data,java,relnoteIntroducing Apache DataFusion Java 0.1.0 - Apache DataFusion Blog
golang,systemP99 CONF 2025 | Parsing Protobuf as Fast as Possible by Miguel Young de la Sota - YouTube
java#JavaNext Language Features - YouTube
javaHow Netflix Uses Java - 2026 Edition - YouTube
javaJDK 8 to 25 Without the Pain - Engineering a Modern Java Platform - YouTube
javaJava 26: Better Language, Better APIs, Better Runtime - YouTube
javaJava AOT in Production at Netflix - YouTube
javaJava Memory Management Best Practices - YouTube
javaJava is Memory Efficient - Inside Java Podcast 59 - YouTube
javaNative and AI Interoperability with JDK 25 and the FFM API - YouTube
javaOn HotSpot Error Files and Useful Tools - Mostly nerdlessMostly nerdless
javaPractical Python AI in Java with GraalPy - Sachin Pikle - YouTube
javaQuarkus Community Call: Quarkus 4 - Migration to Vert.x 5 & SmallRye Common I/O archive support - YouTube
javaReflecting on HAT: A Project Babylon Case Study - YouTube
javaSIMD Vectors in the HotSpot JVM - Auto Vectorization and the Vector API - YouTube
javaScaling ArchUnit with Nebula ArchRules | by Netflix Technology Blog | May, 2026 | Netflix TechBlog
javaSpring Native: The Future of Fast and Efficient Spring Applications by Alina Yurenko @ Spring I/O - YouTube
javaThe .join() That Should Be a Bug | Kronotop
javaThe Java Agent Skills Kit - by Markus Eisele
java,rust10 Things Rust Could Learn from Java (and 5 Things Java Should Probably Steal Back) by Scott Gerring - YouTube
java,systemAPI Gateway Performance Doubling in Practice (English) · GitBook
java,systemApache Cassandra Contributor Meeting | June 10th, 2026 - YouTube
java,wasmBytecode Alliance — Endive and the Next Chapter of WebAssembly on the JVM
java,wasmEndive: Java Virtual Machine meets WebAssembly on CNCF wasmCloud - YouTube
jvmConcurrency Patterns for Modern High Performance Kotlin Servers | Bowen Feng - YouTube
jvm,systemSandboxing Script Extensions with GraalVM | by Christian Humer | graalvm | Jul, 2026 | Medium
miscHow I Doubled eBay’s Productivity - And Still Got Fired - YouTube
rust,systemRust zero-cost abstractions vs. SIMD
rust,systemSlateDB: An Object-Native LSM for Online Systems — SlateDB
rust,systemTokioConf 2026 - Building a Production Control Plane with Tokio by Pooja Kale - YouTube
systemAWS re:Invent 2025 - Large-scale software deployments: Inside Amazon S3’s release pipeline (STG352) - YouTube
systemEveryone gets faster writes: We turned off FPW's in Neon
systemFinding zombies in our systems: A real-world story of CPU bottlenecks | by Pinterest Engineering | Pinterest Engineering Blog | Apr, 2026 | Medium
systemFrom fork() to Fleet: Designing an Agent Sandbox Cloud — Abhishek Bhardwaj, OpenAI - YouTube
systemHow OpenAI delivers low-latency voice AI at scale | OpenAI
systemMany Databases 1 LSM Engine - OpenData - YouTube
systemP99 CONF 2025 | Building a Fast Lock-free Queue for Trading Systems by Sarthak Sehgal - YouTube
systemP99 CONF 2025 | Squeezing Every Millisecond: How We Rebuilt the Datadog Lambda by AJ Stuyvenberg - YouTube
systemSupercharging Redpanda Streaming with profile-guided optimization
systemThe performance bug hiding in our billing settings | Oblique
systemUnlocked Conference — Towards Faster Inference, Daniela Miao (Momento) & Samuel Shen (Tensormesh) - YouTube
systemUnlocked conference - Beyond fork: Memory-Efficient Snapshots for Valkey - Jim Brunner (AWS) - YouTube
systemZero Bloat Postgres Queue | Scaling Postgres 416 - YouTube

Until next time!

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.