bobkb

It’s used by Indian companies and also they provide digital signature services. It’s a widely used platform.

How long does it take to load ?

On my RTX 5000 Pro + 128GB RAM machine its taking 10+ minutes to load.

IMHO Google first needs to make it easy for humans to find where to find the models and its documentation. With aistudio/model garden / Gemini enterprise etc it takes minutes to find the model.

Coding just a stage in the software development. Design and specifications which can help in coding is not solved at all and may never - the end result is software reliability is not a solved problem.

Excellent read! We were talking about the effects of AI Psychosis on companies. It looks like people have a misconceived notion that now work can be done 90% faster and they are attempting to literally get rid of 90% of the engineers. The end result, no one left with do the work - especially when an edge case/disaster strikes or the customer wants something different.

Is their CI and CD workflow robust like gitlab/github ?

Same experience. It took minutes to find the API key from the console.

Now following up with Google support team without luck to find the logs. Prompts send to the model and the responses including the thinking was available in the ai studio. But it’s unclear where to find the same in console.

To make matters worse there is vertex api and rebranded to Gemini something and making it very confusing.

They have support for long format and the industry is already attempting long shots with Seedance 2.0 which cannot be efficiently done without drift in other leading models like Kling.

Happy to see an open weight model ! This has all the right ingredients for success.

Would love to collaborate

Thank you! Jetstream looks useful. For a platform which is production our Redis costs are getting prohibitively high. I was on the look for alternatives and perhaps in the software reliability tool [0] I am buildin try out Jetstream.

[0] https://github.com/bobinson/vulture

Wow!

I have a stupid question - as I understand NATS works very well as a “message” pipe/bus. Anyway to get Redis type cache functionality as well ? Is it something possible ?

We are in the same decision making stage and trying to choose between rabitMQ, Kafka, NATS and some solutions built on top of Redis.

Did NATS eventually worked well?

Not sure whether this is the right step. Rather steps must be taken to ensure reliability of the software irrespective of whether it’s “written” or “generated”. The code generation based on “rules” is not new after all.

The leader boards based on token usage happened in our org for a month. Then we managed to convince the board that what matters is the reliable software shipped. Now we are back to DORA metrics.

How to prove this is indeed the system prompt against a certain timestamp (in the past ) ?

Very useful. The whisper setup is something similar to what we have been using. The LLM setup though is outstanding.

This gave me shivers

Will it be accessible to anyone ?

This type of attack is going on for few years now. I had 2 in my credit.

Some details https://freebird.in/malicious-code-source-code-shared-via-jo...

Will cursor launch a CLI tool like Claude/codex/opencode/pi ?

I assume your idea is, if the spec and the proof is verified the code generated is good enough as well ?

I have been testing formal verification methods with multiple products. It will be great to also understand more about what’s tried and how it was done. For example attempting to verify the spec is what I have been trying to implement.

In an interesting coincidence I ended up watching Person of Interest S4 E5 while reading the announcement. The series showed some code supposedly belonging to to an AI.

Fable 5 said the first screen shot is from “ IDA Pro’s Hex-Rays decompiler” and a windows driver. The second screenshot triggered the safety guard rails and pushed me into Haiku.

Apparently the code is Windows driver code.

It’s impossible to write a spec that’s not ambiguous , complete and correct in natural languages. Thus prompts will always generate unreliable software.

IMHO even if we are using auditing tools I believe we must use deterministic tools for critical analysis like this. Such rule and pattern based systems may not scale beyond certain point but they can be accurate.

At work we are now in the process of migrating away from Figma. We had spend years perfecting our Figma based design workflow. Currently we are moving all the designs into the code itself using Storybook. The gap currently is reviews and feedback which is addressed by Chromatic now.

I tried building a deliberately vague project around managing MCP servers [0]. The purpose was to find what LLMs and agents can do. While the project didn’t reach anywhere I was amazed by how it’s possible to navigate even with no clear direction. The ability of the “glorified auto-complete” system to pull off something this sort was an eye opener for me.

0. https://github.com/bobinson/aop1

False positives from the deterministic audits a very difficult problem to address. Comparing and deduplicating across different methods or LLM audits seems to the only way.