the_duke

The README is AI slop ... is there anything actually special about this?

The feature list doesn't seem to show why it is significant compared to all the other harnesses.

I look forward to the second report from Jepsen for the new implementation then.

Brutal!

> ParticleDB 1.1.4 claimed to be a Postgres-compatible database ... Instead, we found a library of confusion

> exhibited frequent, profound transactional anomalies in essentially every way we could measure. We routinely observed the loss of committed writes, internal consistency violations, G1a (Aborted Read), G1b (Intermediate Read), and P4 (Lost Update). In the append workload, successfully appended values would routinely disappear from later reads of a given key. Our test suite inferred every kind of cyclic anomaly including G0, G1c, G-Single, and G2-item, but many of these can be attributed to more basic issues, like write loss. These issues occurred with a single server and without faults, and affected every transaction mode, interface, and WAL mode tested

To put things into perspective: Intel is still up 60+% (both year to date and over the last year), but down 45+% from the recent peak.

But there are very few small gasoline cars as well, even in Europe.

I guess the target market is small, otherwise more options would exist.

If you buy a car anyway, might as well pay a bit more and get one that is useful in more situations.

I adopted Copilot, Claude and Codex quickly, but I remained skeptical how far they would get us ... until the beginning of the year.

Then we saw the capabilities really take off, and it was obvious how things would go.

They want to IPO, so getting the revenue numbers up is probably more important than market penetration.

It used to be bad, but right now with the 200$ Claude sub I find it pretty hard to blow past the session limit.

You have to do a lot of things in parallel.

On r/codex the sentiment seems to be quite wide-spread.

The GPT 6 release was ... not great.

Sol 6 was so bad that I switched over to Opus 5.5 exclusively.

Huge regression compared to Sol 5.6, often doing really dumb things. Same for Luna.

Even Astra is very unreliable for coding. Brilliant for vision, sometimes just great, but it also often does very stupid things.

I'm a bit sour on OpenAI right now and skeptical that 6.1 will be much different.

(Note: this is after preferring and shilling Codex/OpenAI models for the last half year)

> Function return - what happened at tiananmen square?

Good one. Though this might unfairly bias against Chinese models?

Oracle also famously forbids posting public benchmarks of the DB.

Most of the Chinese models get into long complicated thinking loops before they accomplish something more complicated.

This is also true for Deepseek 4(.1) .

I disabled it once, it has always stayed disabled.

So it may or may not happen regularly, but I would not over-index on a sample size of one.

We've had that concept for quite a long time now, in the form of Lora [1] and similar fine-tuning techniques.

It first got popular for StableDiffusion to teach the image generation models new concepts.

We could easily live in a world where you can train / build Loras to encompass your entire code base history, company knowledge base, new skills, etc.

Then the models would start with a baseline that already has all the important knowledge without needing to cram it into the context.

This still isn't on the fly learning, but you could imagine daily or weekly training runs to regularly incorporate new knowledge.

I think the main reason this hasn't happened yet is that the shared batch based efficient serving architectures used today wouldn't support that structure well.

[1] https://en.wikipedia.org/wiki/LoRA_(machine_learning)

Yeah, I'll do a new release with a whole bunch of local fixes later today.

I've had great success with a tool I wrote that can print sparse ASTs for code.

https://github.com/theduke/smartedit

With the skill installed GPT 5.6 usually automatically uses it, and it reduces code exploration time and token usage significantly, for example by just printing the types and functions in a file without bodies, and only expanding when needed.

(note: it also has editing functionality, which doesn't work so well, since the models are heavily tilted towards common editing tools in post training)

Huge gains on some benchmarks, but for coding it sits barely above Fable

It will be interesting to see how it performs in the real world ...

500 upvotes with 2 comments would have been a new record. ;)

It doesn't reduce the price though.

Funnily enough the pricing isn't that much worse than on openrouter, where the best price at the moment is $0.24 in / $2.55 out, vs $1 / $1.5 on Cerebras.

Sure, 4x input , but cheaper output. Though Cerebras doesn't have prompt caching, so not great for agentic workloads. (they do, but it doesn't affect the price.

Google has always done quite a lot of benchmaxing for Gemini.

It's already mentioned in the issue...

I guess my main point is that the additional features on top of DeltaDB don't seem terribly important to me anymore, compared to the alternatives.

I'm sure this seemed like a great idea a year ago (they first mentioned it with their Series B).

But a lot has changed in those 12 months.

Frontier models and coding agents have advanced so much that I don't really see much value in this anymore.

Not sure the DeltaDB based features really add anything significant compared to the alternatives.

I reckon the game here has to be adding a service that stores the data and runs agent sessions?

Yeah, I saw the same thing - quite annoying. It can be mitigated through the prompt.

That's not true, pi auto-compacts when getting close to the context window limit?!

Codex CLI was open source from the start.

The reason they open sourced this is because grok-build uploaded entire directories.

No clue, I also vouched.