🇨🇦 Backend Brat. Distributed Diva. Relentless Learner.

Canada
Joined June 2009
Kelly Sommers retweeted
Thank you for making TLA+ a useful skill for a broad community @poteto ! It's great to see the impact of the skills. Post more! TLA+ has a deep stack. Consider using TLC, Apalache, and TLAPS. For example, we can formally prove (or disprove) the correctness of TLA+ specs lifted from code; agents make it incredibly automatic. We can also check model-code conformance (to make sure the model faithfully describes code). The #Specula project has a set of skills you might find useful, github.com/specula-org/Specu…
lauren
@potetoOct 21
there's something really cool about asking your agent to draw a statechart (hypothetical example in this image), then using the /create-formal-verification-skill you made with pstack to find bugs through something like TLA+. you can both understand your code better and how the user flow should work, and fix issues users would otherwise see
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Kelly Sommers retweeted
It's great to see more and more folks starting to use TLA+. @lemmster
/sesh/null
@nerdsane18h
we've been trying this approach of skill based generating TLA+ at @datadoghq for quite some time. wrote a few blogs on it. some of my team affectionately made a sesh-mode skill in one of our oss repos. Added all links in comment. We now have something more advanced, will share soon. a meta-skill that generates the whole suite is a great idea. antithesis skills do something similar for DST, research properties > workload > triage
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Kelly Sommers retweeted
Before blaming S3 for slow writes, benchmark your WAL. We used LAION to benchmark storing multimodal data with a WAL. Lance writes blobs directly using Lance Blob v2, so SSTable flushes contain only metadata. At concurrency 32, Lance delivered 11.2% more write throughput than SlateDB while persisting 48.2% fewer bytes on S3. na2.hubs.ly/H08wr9H0
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Kelly Sommers retweeted
Antithesis
@AntithesisHQ13h
Datadog are doing very cool stuff in this space, worth a follow!
/sesh/null
@nerdsane18h
we've been trying this approach of skill based generating TLA+ at @datadoghq for quite some time. wrote a few blogs on it. some of my team affectionately made a sesh-mode skill in one of our oss repos. Added all links in comment. We now have something more advanced, will share soon. a meta-skill that generates the whole suite is a great idea. antithesis skills do something similar for DST, research properties > workload > triage
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Kelly Sommers retweeted
No repo left behind. We’ve replicated 99.99999% of our Git repositories on Azure. The last 0.00001% are our busiest. This new blog post explores how changing the way our quorum serializes writes opens the door to replicas that keep no persistent state of their own. github.blog/engineering/arch…
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Kelly Sommers retweeted
the compression time is actually significant portion of the request/response time. this implies that the coding harnesses should be compressing up to the previous message in the background/caching and then completing it when the new message comes in.
openai uses zstd which might be a little better
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Kelly Sommers retweeted
I've uploaded a video of my "Vacuuming Large Tables" talk to youtube.com/watch?v=ylqJ5CY2…. I did that talk a bunch of times but I think this was the only recording. If you're coming to my REPACK talk next week at PGConf EU, bonus points if you watch this one first. #postgres
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Kelly Sommers retweeted
Our CEO, Max, officially announced that TiDB X will soon be open source.
🤖 Made with AI
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Kelly Sommers retweeted
"In scale testing, one cluster ran over 6,700 pods across more than 100 node pools, with the busiest nodes holding 15 blocks (240 IPs) each." planetscale.com/blog/planets…
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[new blog post] Atlas Infinite: MongoDB's Disaggregated Architecture muratbuffalo.blogspot.com/20…
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Kelly Sommers retweeted
Today we're open-sourcing pg_vectorsearch, a new vector search extension for PostgreSQL. The performance improvements over HNSW are pretty remarkable. At 100 million vectors: ◆︎ Faster queries, 4× less RAM: 6 ms vs. 8 ms at 95% recall, using 128 GB vs. 512 GB RAM. HNSW couldn't even build on 128 GB. ◆︎ 7× smaller indexes: 25 GB vs. 180 GB at 100M vectors. ◆︎ 50× faster index builds: 49 minutes vs. 42 hours on identical hardware. And at 1 million vectors, PRISM delivers 15 ms queries at 99% recall on just 2 GB RAM, versus HNSW's 392 ms on 4 GB. What leads to these improvements? Rather than traversing an HNSW graph with random memory accesses, our new PRISM index partitions vectors based on distance, compresses them using RaBitQ, and scans them efficiently using SIMD vectorization techniques. This takes better advantage of modern CPUs and memory locality. This is actually our second open-source vector index. We learned a lot from building DiskANN-based pgvectorscale, including some important limitations of graph-based approaches. Those lessons convinced us there was a better architectural foundation, so we started over. pg_vectorsearch is still in alpha, with more engineering ahead. But we believe fast, scalable vector search should live alongside your relational data, without requiring a separate vector database. Released under the permissive PostgreSQL License, just like our pg_textsearch extension for BM25 keyword search. Together, they provide the building blocks for hybrid vector + keyword search in PostgreSQL. Feedback and contributions welcome! Code and blog post below. ⚡️⚡️⚡️
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Kelly Sommers retweeted
PlanetScale
@PlanetScaleOct 10
AWS's own docs say gp3 volumes can drop below 90% of provisioned IOPS 1% of the time. That's 86 hours a year, per volume. Spread a database across 768 of them and there's a 99.65% chance one is hurting production right now. planetscale.com/blog/the-rea…
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Kelly Sommers retweeted
Gunnar Morling 🌍
@gunnarmorlingOct 5
Not sure who needs to hear this (well, I guess I am), but „my agent did this“ is never an acceptable answer if a system you built is not working correctly. That bucket stops with you.
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Introducing a new versatile hash table, ~2x faster than rust's hashbrown (SwissTable). Precise capacity sizing and the physical load factor up to & including 1.
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Kelly Sommers retweeted
xitter.cf/mizuki_izuna/status/21… CAPCOM’s next-gen RE ENGINE work is very interesting. They’re basically attacking several pain points at once: C++ complexity, long build/link times, memory corruption and leaks, awkward reflection, duplicated C++/C# type systems, expensive marshaling, IL2CPP debugging, GC pressure, and memory fragmentation. The answer seems to be RE:Runtime + RE:C++. RE:C++ is built around C++23, but with C#-like semantics layered on top: automatic memory management, generics, interfaces, boxing, exceptions, and access to the .NET standard library. The really interesting part is the workflow. CAPCOM is using C# as the frontend language for parts of the runtime. Code is written in C#, compiled into a normal .NET assembly, then RE:Build converts/decompiles that into RE:C++ and builds it into native libraries/executables. So the rough pipeline is: C# → .NET DLL → RE:C++ → native C++ binary That means developers can work with C# syntax, tooling, libraries, reflection, attributes and unit testing, while the final game/runtime code still ends up native. They’re also doing their own memory-management work underneath this. Instances are managed in 64 KB pages, with page reference counting plus cache and GC optimizations. The goal seems to be reducing the cost of managing huge numbers of objects while improving locality and avoiding some of the fragmentation problems they’ve had before. This also makes CAPCOM’s REDox project much more interesting. REDox is their open-source .NET serialization/data library, with JSON, CBOR, MessagePack-style structured data handling, lazy decoding, streaming, compact token storage, etc. At first glance it just looks like a standalone .NET library. In context, it looks much more like the kind of infrastructure they can write and test normally in C#, then feed into this RE:Runtime / RE:C++ pipeline and ship as native engine code. #RE2026CAPCOM #CAPCOM
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Kelly Sommers retweeted
A new hire told me -Wall -Werror was mandatory in their C++ assignments at school. The world is healing.
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Kelly Sommers retweeted
One of the best follows on here. My earliest memories of Kelly were being annoyed at her for shitting on GraphQL... Years later I realized she was just ahead of the curve 🙇‍♂️
It’s NOT a hot take that we will be reviewing less code soon But today you shouldn’t follow loud voices who have been full sending some of the worst code we’ve ever seen Stick with Ruby or any language for that matter if your team is executing Use AI to elevate your weaknesses
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Kelly Sommers retweeted
Antithesis
@AntithesisHQSep 29
The Event Platform team at @datadoghq have been rebuilding the pipelines that bring customer data into their platform with Antithesis' help. "Antithesis caught protocol-breaking bugs in stateful encoding, but what really gave us confidence was seeing it exercise complex timing interleavings that would have been difficult for us to reproduce with our existing tests. The failures it surfaced made us reevaluate how we were testing the system." -- Joy Zhang, Senior Staff Engineer at Datadog Full story below.
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