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 ❤️
JP Hwang retweeted
rediscovering good practices from first principles
impressive
Picked up a copy over the weekend from a bookshop and so far, I have mixed feelings.
Is anyone else reading this? Wdyt?
The author of “The AGI Chronicles” says his book “was written by a very tired, overworked, under-slept human being.” buff.ly/eX6sTxn
JP Hwang retweeted
Reminder that an anonymous Theranos employee discussed the whole fraud (without naming the company) on a popular subreddit years before the WSJ broke the story
JP Hwang retweeted
guy who lives among some of the world’s most interesting sociopaths: these sociopaths are boring
JP Hwang retweeted
how do you fine-tune a decision model on your own data?
in these two notebooks, you'll learn:
• how to prepare the training data
• how to fine-tune our d1-3B decision model
• using @UnslothAI and @huggingface TRL
all within 30 minutes on a free-tier GPU.
unsloth: github.com/Liquid4All/cookbo…
TRL: github.com/Liquid4All/cookbo…
ps: how adorable are these halloween kitties? 👻
Changing jobs as a remote work robs you of the rituals that mark the occasion.
Packing and unpacking boxes, giving and receiving badges, or the goodbyes then hellos - all nowhere.
In lieu of a that, I re-painted and rearranged the office. New surroundings for a new start.
JP Hwang retweeted
You're not going to believe what happened today.
I am incredibly happy to welcome @_jphwang back at Weaviate! 🤩
So I'm starting a new role at @weaviate_io as a Lead Developer Experience Engineer - with a focus on agent experience.
I will say more about what the heck that all means; but for now, I wanted to relay my gratitude for my previous role, and excitement about the opportunity.
JP Hwang retweeted
poteto's cursor profile is public. She's used 1.5T tokens in the last 30 days.
Assuming an avg rate of $8/M that's $384k/day or $11m. In the last 30 days. Per year thats $132M.
Let's assume Cursor has some insane deal with Anthropic and gets 90% off. That comes out to $38k/day or $1.1M in Sept/Oct.
$13m will get me 26 sassy staff-level all stars for $500k/yr
Readers added context they thought people might want to know
The Cursor profile at cursor.com/@lauren shows 1.5T tokens used. Cost estimates at $8/M do not account for prompt caching common in agent coding, where cache reads are billed at ~10% of input rates per analyses of Anthropic pricing. This results in lower effective costs.
cursor.com/@lauren chudi.dev/blog/claude-pr… carlosaragon.online/blog/anthropic…
JP Hwang retweeted
“.. Each hour, Muse updates its dossiers on you and the people you’ve mentioned in chats, messages, and emails that Muse has read. .. Even people who don’t use Muse are subject to this mapping process by other people’s Muse agents.”
@time $META
time.com/article/2026/10/06/…
JP Hwang retweeted
Baffling how bad startup tech blogs are getting: more of them are AI nonsense.
I know because every now and then a marketer emails me, hoping to get coverage.
More of then not it's overly verbose, impossible to follow... AI slop. That seemingly no one noticed?!
Feels like no one wants to put in the work
it's easy to fall into the trap of micromanaging your agents instead of correcting the environment that shapes its behavior.
pstack 0.15.9 now ships a new /correct skill: if you keep correcting agents for the same mistakes, it finds the pattern and fixes it with architecture, types, and checks.
i like to imagine that commits rapidly flowing into the codebase are like a bonsai tree rapidly growing in whatever direction it pleases. what you want to do is not to give in to the chaos, but tame it intentionally with the right constraints
0.15.9 also has improvements to:
• /architect: now includes instructions on designing agent friendly architecture
• a new /benchmark-checklist skill based on @brendangregg's benchmarking checklist
here's a prompt you can give to grok bot to start architecting a better codebase with pstack:
/poteto-mode create a new Project agent to refactor and rearchitect my repo so that our architecture is more agent friendly. it should use both the /correct and /architect skill to look through past commits and find the most common pitfalls that agents fall into, whether its with code quality, performance, or bugs. if relevant you can also use the /recall skill to find past conversations for context. it should make a high level plan for me to review. it should also answer its own open questions by prototyping rather than defer to me. come back to me with a plan backed by real data. once i approve the plan, use either the stack or full autopilot playbook (ask me which i want) to execute
every time you intervene and correct your agent, you should think about how to eliminate it entirely.
in order of value:
1. categorically eliminate the problem through better architecture or choice of data structures
2. turn it into a lint rule or test so CI catches it
3. turn it into a skill or rule
4. have humans review the code to catch it (ngmi)






























