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1M context is now generally available for Opus 4.6 and Sonnet 4.6

claude.com

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Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#151
post #128

Opus 4.6 is nuts. Everything I throw at it works. Frontend, backend, algorithms—it does not matter. I start with a PRD, ask for a step-by-step plan, and just execute on each step at a time. Sometimes ideas are dumb, but checking and guiding step by step helps it ship working things in hours. It was also the first AI I felt, "Damn, this thing is smarter than me." The other crazy thing is that with today's tech, these…

Opus 4.6 is AGI in my book. They won’t admit it, but it’s absolutely true. It shows initiative in not only getting things right but also adding improvements that the original prompt didn't request that match the goals of the job.

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Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#152

Earlier quoted context omitted.

Is it ever useful to have a context window that full? I try to keep usage under 40%, or about 80k tokens, to avoid what Dex Horthy calls the dumb zone in his research-plan-implement approach. Works well for me so far. No vibes allowed: https://youtu.be/rmvDxxNubIg?is=adMmmKdVxraYO2yQ

That video is bizarre. Such a heavy breather.

Most of that is just nervousness

Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#154
post #124

Earlier quoted context omitted.

Thanks for the video. His fix for "the dumb zone" is the RPI Framework: ● RESEARCH. Don't code yet. Let the agent scan the files first. Docs lie. Code doesn't. ● PLAN. The agent writes a detailed step-by-step plan. You review and approve the plan, not just the output. Dex calls this avoiding "outsourcing your thinking." The plan is where intent gets compressed before execution starts. ● IMPLEMENT. Execute in a fresh…

That's fascinating: that is identical to the workflow I've landed on myself.

It’s the style spec-kit uses: https://github.com/github/spec-kit

Working on my first project with it… so far so good.

Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#155

Opus 4.6 is nuts. Everything I throw at it works. Frontend, backend, algorithms—it does not matter. I start with a PRD, ask for a step-by-step plan, and just execute on each step at a time. Sometimes ideas are dumb, but checking and guiding step by step helps it ship working things in hours. It was also the first AI I felt, "Damn, this thing is smarter than me." The other crazy thing is that with today's tech, these…

The replies to this really make me think that some people are getting left behind the AI age. Colleges are likely already teaching how to prompt, but a lot of existing software devs just don't get it. I encourage people who aren't having success with AI to watch some youtube videos on best practices.

Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#156

Opus 4.6 is nuts. Everything I throw at it works. Frontend, backend, algorithms—it does not matter. I start with a PRD, ask for a step-by-step plan, and just execute on each step at a time. Sometimes ideas are dumb, but checking and guiding step by step helps it ship working things in hours. It was also the first AI I felt, "Damn, this thing is smarter than me." The other crazy thing is that with today's tech, these…

What kinds of things are you building? This is not my experience at all. Just today I asked Claude using opus 4.6 to build out a test harness for a new dynamic database diff tool. Everything seemed to be fine but it built a test suite for an existing diff tool. It set everything up in the new directory, but it was actually testing code and logic from a preexisting directory despite the plan being correct before I tol…

Curious what language and stack. And have people at your company had marginally more success with greenfield projects like prototypes? I guess that’s what you’re describing, though it sounds like it’s a directory in a monorepo maybe?

Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#157
maybe i'm thinking too small, or maybe it's because i've been using these ai systems since they were first launched, but it feels wrong to just saturate the hell out of the context, even if it can take 1 million tokens.

maybe i need to unlearn this habit?

Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#159

It’s interesting because my career went from doing higher level language (Python) to lower language (C++ and C). Opus and the like is amazing at Python, honestly sometimes better than me but it does do some really stupid architectural decisions occasionally. But when it comes to embedded stuff, it’s still like a junior engineer. Unsure if that will ever change but I wonder if it’s just the quality and availability of…

I've had a similar experience as a graphics programmer that works in C++ every day

Writing quick python scripts works a lot better than niche domain specific code

Re: 1M context is now generally available for Opus 4.6 and Sonnet 4.6

#160
there is a parallel between managing context windows and hard real-time system engineering.

A context window is a fixed-size memory region. It is allocated once, at conversation start, and cannot grow. Every token consumed — prompt, response, digression — advances a pointer through this region. There is no garbage collector. There is no virtual memory. When the space is exhausted, the system does not degrade gracefully: it faults.

This is not metaphor by loose resemblance. The structural constraints are isomorphic:

No dynamic allocation. In a hard realtime system, malloc() at runtime is forbidden — it fragments the heap and destroys predictability. In a conversation, raising an orthogonal topic mid-task is dynamic allocation. It fragments the semantic space. The transformer's attention mechanism must now maintain coherence across non-contiguous blocks of meaning, precisely analogous to cache misses over scattered memory.

No recursion. Recursion risks stack overflow and makes WCET analysis intractable. In a conversation, recursion is re-derivation: returning to re-explain, re-justify, or re-negotiate decisions already made. Each re-entry consumes tokens to reconstruct state that was already resolved. In realtime systems, loops are unrolled at compile time. In LLM work, dependencies should be resolved before the main execution phase.

Linear allocation only. The correct strategy in both domains is the bump allocator: advance monotonically through the available region. Never backtrack. Never interleave. The "brainstorm" pattern — a focused, single-pass traversal of a problem space — works precisely because it is a linear allocation discipline imposed on a conversation.

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