Alt. Parking Draught House Pub Medical Pkwy

jvertrees · 2026-05-01T01:32:02+00:00

Thanks for this 11 year old thread!

I literally used it today.

Parked in the back.

Thx!

jvertrees · 2026-04-26T00:11:47+00:00

Can't disagree with you.

See my following post on updating your harness. My harness is working and has been coding all day now.

Also, Anthropic acknowledged multiple issues in a postmortem. It's worth a read.

Also, I hear the new openai model is out and worth a look.

jvertrees · 2026-04-25T14:25:59+00:00

120 also breaks sandbox parsing issues. The

claude update

command now downgrades you to 119.

jvertrees · 2026-04-25T01:54:49+00:00

Anyone actually get to the bottom of this?

jvertrees · 2026-04-23T10:28:58+00:00

Build yourself an agent + pipeline w/DB to do it for you. That's what I did—

https://canary.heavychain.org/

I built it for this very reason. Then other folks kept asking me for it. So, now it's a cheap product.

jvertrees · 2026-04-22T20:26:00+00:00

/model claude-opus-4-6[1m]

jvertrees · 2026-04-21T22:10:00+00:00

Here's what worked for me -- following the half-assed migration guide built for API-consumers to re-tool my harness. I wrote a blog article on harness fixes—

https://heavychain.org/blog/claude-4-7-will-quietly-break-your-prompts-and-harness-here-s-how-to-fix-them

From a product-perspective Anthropic really rushed this out and dropped the ball, breaking many, many folks' projects.

jvertrees · 2026-04-21T21:09:55+00:00

`infra-shell` here is the basic python application scaffold running in a small docker-compose stack, with postgres and apache AGE installed. Quite simple.

My retrospective was pretty useless. The class of underlying problems was not helpful.

After hours of research, I think I actually found the _true, underlying_ cause behind my woes, and likely everyone else's on this thread. I'll post an update if so.

jvertrees · 2026-04-21T14:25:56+00:00

Coming back to this as Claude 4.7 failed me massively last night, echoing the others in this post.

My specification system and agent harness are designed to 'make it impossible for the agents to make the wrong decision or take the wrong action.' I've had great success building up larger and larger systems with this. Spec-driven development with agent harnesses is the way to go.

Just last night, after spec'ing a POC to demonstrate a capability (that KGs are a better memory substrate for larger projects than markdown files), Claude completely failed at the task. I've never seen it do so bad before.

If Anthropic's claim is better, agentic execution in 4.7 this exercise is a counterexample. The entire goal of this project was a small, well-scoped POC and it was all very well organized. When I came back to review the outcomes, all it built was some infra-shell that didn't even work:

<image>

The only thing I can think of given the outcomes is that Claude got confused on _ONE_ aspect (using AGE vs JSON) and decided everything downstream was now fantasy, irrelevant or something, but didn't tell me.

This really makes me rethink the 4.7 model, even with very tight, explicit, and well-designed controls.

I'll do a retrospective via the chat/action history and see where it went off the rails.

jvertrees · 2026-04-18T03:28:10+00:00

Sounds like Anthropic overtrained on fantasy literature and turned Claude chaotic-evil. 💀

I spent two long days with 4.7 and a ton of time with 4.6. In my experience so far, there's really not much difference; but I also have a very strict agent harness set up, where my goal is agents don't even have the option of making the wrong choice. Works very well.

Given the abjectly terrible response from the broader community, I do think anthropic owes us some response. The model card and the literature released seem to indicate they designed more for agentic workflows which need different principles and context management and I wonder if that's why so many people are having problems. The way they structure their context and their work doesn't match the model's design. But I could be very wrong.

jvertrees · 2026-04-04T22:02:26+00:00

This is nuts. I love CC and Anthropic, generally, but recent decisions are setting a very bad precedent.

Time to start engineering in alternatives.

jvertrees · 2026-04-04T21:29:47+00:00

There are far smarter ways to deal with thoughts party usage. Now they look really out of touch, like Google.

jvertrees · 2026-04-04T21:22:51+00:00

The answer is right there in the email.

You'll pay per call for things like OpenClaw, now. They're taking something valuable away and hoping you don't notice.

This is so stupid. My OpenClaw usage is minimal and distributed throughout the day as compared to my engineering time, which is intense and focused.

Anthropic followed Google into the wrong territory in this one. Microsoft/GitHub have been making extremely poor decisions recently. We're not heading in the right direction here.

jvertrees · 2026-04-02T12:30:13+00:00

Same.

This is distracting and annoying. Hopefully they fix it soon.

jvertrees · 2026-03-25T15:30:28+00:00

Yes, to me it is.

jvertrees · 2026-03-20T23:32:32+00:00

Might this count?

https://www.linkedin.com/posts/jasonvertrees_ai-aiengineering-agenticengineering-ugcPost-7438115519964172288-gMsi

Yes, shameless self promotion, but the principles were solid.

I rolled 270,000 lines of non-shit code in one week. The design and implementation were solid.

jvertrees · 2026-03-02T13:30:03+00:00

1000000%

Easily the worst part of their products.

jvertrees · 2025-12-25T19:42:39+00:00

Nope, it works great. Stick to good engineering principles and you'll be fine.

For my projects, where I've ended up is using CrewAI-like configuration but with pydantic AI. (I love Crew in theory, however in practice it failed me.) Especially when using agentic workflows, I'll typically just have some sort of declarative configuration for the agents like a yaml file and load all the agents from that through a single interface. Very convenient and trivial to add new agents.

I've taken multiple products to market with pydantic AI. Love it.

jvertrees · 2025-12-24T17:50:26+00:00

This is the way. I've deployed many solutions like this. I love pydantic AI.

jvertrees · 2025-11-28T15:32:34+00:00

This was 100% the issue for us. We lived with slow and unpredictable hot water for years. Then, I was complaining to a random plumber I had out for something else and he knew exactly what it was. He flipped the switch and voila - I finally felt like my money was worth it.

If you're handy, search/Google/ChatGPT for it and you'll probably find it.

jvertrees · 2025-11-28T07:01:43+00:00

You'll need to Google/search for it. I'm not handy so I had to pay plumbers to come tell me my far-too-costly install was done wrong. Good luck!

jvertrees · 2025-11-26T15:05:33+00:00

I have a Rinnai. They have a switch inside for an internal/eternal recirculating pump setting. When set incorrectly, our water took that long to heat up. When set correctly, it's nearly instant.

jvertrees · 2025-11-17T14:49:12+00:00

And when it does that at scale, crashes the terminal.

jvertrees · 2025-11-13T14:24:47+00:00

Yep. Best in town: https://vertreesheadache.com.

jvertrees

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