Trying to read a book
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Joined November 2019
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Hi there, my name is JP. I’m a developer educator.
I create:
- courses datacamp.com/courses/end-to-… (@datacamp), linkedin.com/learning/vector… (@linkedin learning)
- educational diagrams (jphwang.com/gallery/) and projects (github.com/databyjp/weekend_…)
- blogs & videos.
Let’s connect ❤️
My Friday hot take is that in software engineering there is too much talk of *verification* (is a thing correctly implemented), and not enough talk of *validation* (is the right thing being built).
In the land of the agents, validation is king.
I've been reflecting on this year & my time at Elastic, and wanted to get it written down. Apologies if a little cheesy.
jphwang.com/posts/on-work-an…
JP Hwang retweeted
This is one of the most important pieces of social science on human and AI interaction that I’ve seen in some time. Experts follow agentic advice the vast majority of the time (causally), but only a tiny fraction credit the agent—majority think they came to decision on their own volition.
Reminds me of the classic Nisbett and Wilson study on people following outside influence without realizing it
“ But as more work is handed to AI—including AI safety research—these judgment calls are the main levers for people's own values to influence outcomes.
If people defer to AI without noticing, then "human in the loop" might provide less assurance than people expect.”
Our @weaviate_io docs site (docs.weaviate.io) runs on @docusaurus.
But did you know that there's an opt-in package from the Docusaurus team, docusaurus/faster?
It dropped our local build time from ~40s to 8s - and 100s to 30s on GH!
It replaces Webpack with Rspack, Babel and Terser with SWC, and the CSS minifier with Lightning CSS. You turn it on with future.experimental_faster: true in docusaurus.config.js.
If you want to try it out, see our diff: github.com/weaviate/docs/pul…
Remember to compare rendered pages before and after.
Here is the official Docusaurus Faster announcement: docusaurus.io/blog/releases/…
JP Hwang retweeted
my gift to Austria on this glorious day!
github.com/weaviate/weaviate…
JP Hwang retweeted
personal take: as much as I'm techno optimist, I genuinely dislike it when people on X dunk on regular folk for criticizing AI
a company has told everyone they will lose their jobs, many companies did layoffs, people have families. on top of saying all the layoffs are necessary so AI proves econ value until it cures the cancer or saves planet but they did anything but those so far
you might be making a ton from AI, many don't. some people also found a ton of purpose from their work which is gone. I'm not saying we should console people but maybe also don't dunk on a frustrated mass? the field is already detested and this is not helping
🤔
Let me get this straight.
A guy keeps getting weirdly targeted ads on his new Samsung TV.
So he breaks into his own TV to find out why. 1.2M people watch.
42 hours later, OpenAI deactivates his account. Reason: "Cyber Abuse."
His words: "i have never used openai to exploit or find vulnerabilities, only local reverse engineering"
Same week, people on r/claude say Claude now cuts off reverse-engineering write-ups MID-SENTENCE:
"A safety check stopped my response partway through creating the playbook"
The TV is fine.
The guy who opened it is the problem.
TIL there's no GH page view for your non-archived repos.
But - thanks LLMs!
github.com/search?q=user:dat…
Finished putting up some of the comics that survived the last two decades in my collection.
(I got the colored frames from @rubixframes - would recommend.)
JP Hwang retweeted
TL;DR:
- Open-weights MoE coding model: 40B total, 2.5B active, Apache 2.0
- Built from scratch in under 4 months mostly by their automated research system, the Eureka Machine
- Main tricks: a sparser shape, the output layer quantized to 4 bits, and custom kernels, including a fused decode kernel on M4 and M5 Macs
- Beats MoE peers like Qwen3.6 35B-A3B on agentic coding: Terminal-Bench 2.1 54.9 vs 44.9, SWE-bench Verified 74.2 vs 73.4
- Dense Qwen3.8 27B still wins on coding, with Terminal-Bench 73.0 vs 54.9
- 125+ tok/s on an M4 Pro MacBook and 150+ on a 64GB RTX Spark at short context; about 69 tok/s on average across a 128K session
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
JP Hwang retweeted
The most revealing part of this conversation between Jon Stewart and Jacob Coxon wasn't Coxon's warning about A.I. doom, but his description of A.I. utopia, and Stewart's incredulous response. I wrote about it — and Francis Fukuyama's "Last Man," and A.I. easily solving the problems that bedeviled my father's field of mathematics for decades — here: nytimes.com/2026/10/18/opini…
Former Anthropic researcher Jacob Coxon (@hilbertspaess) and Jon Stewart discuss AI, jobs disappearing, universal income, and whether tech CEOs are creating a dystopian future
