I switched from Opus 4.6 -> Opus 4.7 -> GPT 5.5 and tried Flash 3.5 tonight and I was not impressed. It is straight up unreliable, e.g. deleting code and forgetting to add the new stuff it was asked to, then happily marking the task as complete with up-beat conclusion. I personally appreciate GPT 5.5 toned-down, objective style so really dislike how this model feels. I get that it's a flash model and not in the same league as GPT 5.5 but their marketing suggest otherwise so thy are just setting themselves up for disappointment.
I’m quite sure that’s the en state of software except without the software around it. There will only be an AI and interface. For now, though, while tokens cost a non-trivial amount of energy, I think you can do something more useful if you have the LLM modify the program at runtime because it’s just may orders of magnitude cheaper. Fx, use the BEAM, it’s actor model, hot code reloading, and REPL introspection and you can build a program that an LLMs can change, e.g. user says “become a calculator” and “become a pdf to html converter”.
I’m not just making this stuff up of course, got the idea yesterday after reading Karpathy’s tweet about Nanoclaws contribution model (don’t submit PRa with features, submit PRs that tell an llm how to modify the program). Now I can’t concentrate on my day job. Can’t stop thinking about my little elixir beam project.
Can you share technical details please? How is this implemented? Is it pure prompt-based, plugins, or do you have like script that repeatedly calls the agents? Where does the kanban live?
I commend the fact they acknowledge the maintainer's work, but seeing the singular 'maintainer', I can't help but notice the weight on that one person's shoulders.
Naming things is hard for a reason. It shapes how we think and communicate. When I'm pointing an LLM at PostgreSQL internals vs. asking it to make a navbar prettier, I'm collaborating in fundamentally different ways. Having distinct terms helps us share ideas and set appropriate expectations.
We need a new term for LLMs actually solving a hard problems. When I help Claude Code solve a nasty bug it doesn’t feel like “vibing” as in “I tell the model what I want the website to look like”. It feels like sniping as in “I spot for Claude Code, telling how to adjust for wind, range, and elevation so it can hit my far away target”.
Thanks, appreciate it. Indeed, and Anthropic did something similar for Google sheets a year ago. I am dying to know why they decided this should not be part of their excel effort. They obviously put a lot of work and thought into claude for excel so it must be intentional.
Anyone from Anthropic here that would like elaborate?
If someone is processing 50k rows, that means they found real value and the UX is working. That's the whole point.
Also, 50k rows wouldn't cost $50k. More like $100 with Sonnet 4.5 pricing and typical numbers of input/output tokens. Imagine the time needed to go through 50k rows manually and math doesn't really work for a horror story.
So cool, I hope they pull it off. So many people use Excel. Although, I always thought the power of AI in Excel would come from the ability to use AI _as_ a formula. For example, =PROMPT("Classify user feedback as positive, neutral or negative", A1). This would enable normal people (non-programmers) to fire off thousands of prompts at once and automate workflows like programmers do (disclaimer: I am the author of Cellm that does exactly this). Combined with Excel's built-in functions for deterministic work, Claude could really kill the whole copy-pasting data in and out of chat windows for bulk-processing data.