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You only need the frontier model for one single edit
- pistoriusp 2mo agoApparently "/prewalk" is built into https://github.com/can1357/oh-my-pi https://github.com/can1357/oh-my-pi, which I guess is a lot like "Oh My ZSH?" Pi with batteries included. There are a lot of interesting ideas in it, mostly none that I have applied to my own workflows. Curious if anyone has used it?
- AshamedBadger56 2mo agoI've played around with all the popular agent CLI's and I've landed on omp/ohmypi as my favorite for now. It's definitely the opposite of a lean install like Pi, but I find it works a lot better than OpenCode. It's also very very active, with a lot of cool new ideas like this prewalk in it.
- the_gipsy 2mo ago> Senior architect, junior engineer. Sounds great, right? Sounds like a consulting slaughterhouse. Picture Java Enterprise Solutions. As we all know, the pinnacle of software engineering.
- deleted 2mo ago[deleted]
- swiftcoder 2mo agoHah. Yeah. Weirdly, most software managers also seem to believe this whole "senior writes a plan, junior implements" is cost-effective (it typically isn't, one should invest in hiring/promoting more seniors).
- peheje 2mo ago[flagged]
- wrs 2mo agoLately I'm trying a variation on this: Have the main agent make a phased implementation plan, then for each phase, have it start an implementation subagent with a focused prompt, then review that agent's work in the main session. The theory being that the main session still contains all the research, but it can review just the diff rather than have the entire implementation session in context as well. The post doesn't include a review in the cost comparison, but I find that immediately doing a review catches lots of mistakes.
- pqdbr 2mo agoI’m using Claude code dynamic workflow like this. I tell Fable to use a workflow. He is the planner, orchestrator and reviewer. Opus agents are implementers. Works unbelievably well.
- danielciocirlan 2mo ago“He”
- oliver236 2mo agocan you explain this in a bit more detail? super interested
- pqdbr 2mo agoIt's literally all there is to it. Write your prompt normally. then, at the last paragraph, you write: Use a workflow to implement this. You (Fable) are the orchestrator, planner and reviewer. Opus agents are implementers. That's all there's to it. it will create the dynamic workflow - has a nice interface native to Claude Code - and do all the coordination to deliver what you asked.
- Almondsetat 2mo agoDid they also use Gemini Flash to write this article? Because, frankly, it's unbearable
- IshKebab 2mo agoVery clearly. Even if you have no qualms with AI I don't understand why people think writing in this cliched style is ok. On the other hand people thought it was fine to use cliched titles like "The unreasonable effectiveness of..", "... for fun and profit", "The rise and fall of ...", "All you need is ...", etc. etc. So I guess there's just a large portion of writers that are immune to cliche? Pretty annoying anyway.
- rwc 2mo agoJust specify that the more efficient model should only be used if the cost of doing the task would be less than using the higher horsepower model. Fable has no problem routing based on that instruction.
- viccis 2mo agoFrom what I can tell, I have a similar workflow. I get Fable 5 / Sol on High to talk to me about requirements until it's ready to design. I then have it break the design into tickets. I use kata, an agent-oriented issue tracker. If it ever starts to get bloated, I'll just make my own as it doesn't need too many features. Anyway, I tell it to include sufficient context in each such ticket to be picked up by a new implementation agent. Explicit user stories, Cucumber style acceptance criteria, etc. When it's done, I switch to a smaller model and tell it to start a /goal of calling `kata ready` to get tickets ready to be worked. Work one ticket on each goal iteration, committing changes when done. Stop when all the tickets are either closed out or are blocked on actions from me. It works fantastically. I can get entire (relatively straightforward) iOS apps done in under my $20/month five hour session window. I get even better results if I do the QA session with the frontier model with two output artifacts: an implementation spec and a design prompt for Claude Design. I push the design prompt through Claude Design, tweak the results, and get a design spec. When I have the frontier model do the planning, I have it read the implementation spec from before, along with the design spec's overview file. When I do it this way, I've done A/B tests between Fable 5 and Sol, and the apps they wrote were basically identical. It's so much cheaper than the "let loose the subagent fleet!" form of context management. I haven't even added any kind of frontier model validation cycle into this loop yet. Everyone keeps talking about a subagent flow in which the big boy reviews the work of the drones, but I've found that if it encodes its acceptance criteria well enough, they do a satisfactory job of it themselves.
- hankbond 2mo agoDoes anyone have a name for that really neat "the bar chart is a bookshelf, you can hover a spine to see the cover" vis? I've never seen it before.
- ClikeX 2mo agoNo idea if it has a name. But the page source is also not giving me anything useful. And it pains me to even look at it.
- can1357 2mo agoThink I unironically spent more time on that than writing the dang thing. Glad you liked it though! Not sure if there's a name, was the best thing I could come up for visualizing turns.
- Bolwin 2mo agoThis is a lot of words to say "switch models instead of writing a plan file and starting a new session" which I do anyway. That said, it takes me a while to reads plans and often I'll take a break, by when the cache has expired. At that point it may be better to start over
- fastball 2mo agoI don't think that is the takeaway at all.
- pphysch 2mo ago> Any agent, any model, any scaffold: the bill is essentially O(reads) Therefore token sellers have incentive to produce verbose, unreadable code
- pixl97 2mo agoWhichever token seller defects can capture a larger chunk of the market with slimmer code.
- pphysch 2mo agoDo you think the average vibe coder or AI-smitten suit cares about slim code? A lot of folks still think "LOC = good" in 2026. It will be a hard sell for the practical engineer. Even if they understand "slim code = fewer tokens = less spend" they still have to grok that less code is not less functionality.
- pietje 2mo agoThey are supply constrained at the moment so they still have incentive to keep it short
- figmert 2mo agoThe reason I use plan then implement is because I can adjust the plan, whereas if I get it to implement straight away, it might (and often does) make the wrong decisions that will be harder to adjust, or I'd have to adjust it after the fact.
- canpan 2mo agoOne big pain point is not seeing the thinking trace. I noticed using pi with a self hosted model that I could spot, stop and correct my prompt much faster. With claude etc I have to first wait for it to think 3 minutes and then notice it got completely off the path I wanted. I still use a plan, because my local model is not as smart as opus, but I can iterate much faster.
- carterschonwald 2mo agothis so true. its really hard to make sure a model isnt going off the rails if i dont see full cot. the fact that oai and anthropic models hide it now has made them less reliable. which is a shame
- trollbridge 2mo agoThe great news is you can use DS-V-Pro, MiMo-V2.5-Pro, GLM-5.2, or K3 and see everything. I tend to use 5.6-Sol now more for one shot type of tasks where all I want is the answer and I’m not going to read the reasoning.
- carterschonwald 2mo ago5.6 sol is pretty good, its definitely very very well tuned, i'm not quite sure which of the others i should use near term, but theyre doing great work
- WilcoKruijer 2mo agoI’ve seen people have success figuring out model reasoning by adding a required `reasoning` parameter to tool calls. Might be worth experimenting with this for the `write` tool call in a coding agent harness.
- nchmy 2mo agoThis entire argument rests upon the fact that Gemini Flash ain't cheap. Try plan with opus 4.8 and then implement with Deepseek v4 flash - its 35x cheaper for reads, 90x cheaper writes, and 18x cheaper cache reads. Or plan with Deepseek Pro, or even Flash itself. I've been impressed with both.
- pdyc 2mo ago+1. i have similar workflow and i use local models on igpu so token cost is free and electricity cost is in cents.
- spuz 2mo agoI cannot imagine local models running on an igpu could get anything close to either a useful plan or execution of a plan. I've tested Qwen 3.5 27B locally and its solutions to coding problems are usually flawed and running in thinking mode is too slow and that's on a discrete GPU. How do you get anything useful done on a model that runs on an igpu?
- nchmy 2mo agoyeah, i dont understand, whatsoever, the folks who are running models locally other than for privacy/curiosity. It is surely FAR slower, more expensive (electricity + hardware costs), and worse quality. Better to outsource to providers who have optimized everything and costs are distributed/amortized across all users. Perhaps someday local llms will be sufficient for coding etc, but it's going to be a while.
- luciana1u 2mo ago[flagged]
- ricardobeat 2mo agoI just did this five minutes ago. Started a task with Kimi K3, noticed cost going up, switched to Minimax M3 continuing from the same context. Really easy to do with Crush/OpenCode. It works quite well most of the time. For very deeply technical tasks, where exploration involves multiple agents (and blowing up context limits a few times), I'll have a plan written down to disk that will be much longer than 2k tokens.
- rolymath 2mo agoWow. Someone using Crush in the wild. What do you think of it? I'm having trouble switching to it because the rest work fine, but would love to actually adopt it.
- ricardobeat 2mo agoIt has the most user-friendly UI of them all, a really usable scroll view. Automatically picks up LSPs. Has a well maintained provider registry. Looks nice and runs fast. The main downside is that subagents cannot have edit tools, which kills any agent orchestration possibilities. I use it for most one-shot / single task prompts using open models.
- potus_kushner 2mo agothis article is full of missing text like this: " of Opus at of the cost, the speed, points over oneshot Flash." is it just me or is that page using some brand new js feature or chromium-only hack?
- Too 2mo ago> The mistake is upstream of the architecture diagram. People price agents the way they price people: senior time is expensive, so minimize senior involvement. > But the expensive part of an agent's day is not the fixing, building, or even the thinking. Opus fixing things does not cost money. Opus reading things costs money. Heh, another rediscovery that agents and humans are alike. By the time I've researched a Jira ticket and made it unambiguous enough to outsource, I might as well have written the code myself instead.
- wordpad 2mo agoYou are more proficient at writing code than ai specs. You could get better/faster at writing good specs and its a higher cap skill now. For low risk changes you could let llm write its own spec from high level requirements and just validate its assumptions/design decisions.
- egamirorrim 2mo agoIsn't part of this the inevitable cache invalidation that comes from switching between providers (Opus to Gemini)?
- Aeolun 2mo agoYeah, but you only pay the switch on the much cheaper model, the first bit is done with smaller context on the expensive model, so you still save money in the end.
- nxtfari 2mo agoThis is really smart, like the author said, old idea but cleverly applied. In case anyone wants a summary: don’t one shot, don’t use plan mode and hand off the plan to cheap executors, ask the frontier model to explore, create a todo list, and then start when it feels confident; stop it after first code edit, then prefill the context to cheap executor to continue.
- brookst 2mo agoHow is that different than just having the frontier model write a build plan and store it, then have a cheaper model execute? That’s pretty normal practice. The build plan should be better than the context that generated it since it strips out wrong turns and other noise. I think?
- xutopia 2mo agoThe article actually explains it better than I can but essentially you do not want the agent to get desperate which causes it to burn extra tokens. Planning phase doesn't test assumptions by going straight in the code and testing out parts of the ideas to execute so it has to consider way more in planning than it would if it could go quicker into the coding phase.
- brookst 2mo agoHmm. When I have Opus or Fable write build plans, then invariably invariably read code extensively, but it’s true they don’t make changes and run tests. I guess the way to formalize this would be to add a “make minimal change to confirm approach” instruction to the build planning prompt. Probably can even parallelize that so as the build planning prompt iterates subagents get launched to validate, similar to what research modes do. I’m a little skeptical, but will give it a try.
- diarized 2mo agoWould it be `/superpowers:brainstorming` + `/superpowers:writing-plans` on stronger model and `/superpowers:subagent-driven-development` on smaller model?
- _ink_ 2mo ago> Below: the share of runs that went poking around the web for it. Filthy cheaters! I can understand that in their benchmark setting they wouldn't want the model to find an existing solution. But in my day to day work, wouldn't I want the model to search online for the best solution? Am I not shooting myself in the foot by preventing it?
- gillesjacobs 2mo agoIn ML, you want to test general capability of a model (generalizability), because you want it to perform well on unseen tasks. In that benchmark, the literal reference is leaking through web search, the agent can see the matching real codebase online and the commits so that's test set leakage. I know no programmer that was ever paid to rewind an existing codebase to a previous commit and implement a feature/fix a bug that exists in the next commits.
- andreyvit 2mo agoIt's hard to argue with the numbers, but starting with a (mostly true!) “research is the most expensive part” premise, this strikes me as an odd direction to go to optimize costs: 1. As others pointed out, we feed all the same research turns to a smaller model, so we pay the uncached price for all of them. 2. During research, the model typically reads more code than is relevant, to figure out what is relevant and what is not. If that's the expensive part, we keep paying for those turns with the most expensive model? 3. There is no quality comparison of the resulting code. Same plan != same code, and AI tokens during the initial implementation phase isn't the only cost attributable to the task. I do the opposite: 1. Outsource research to a subagent, or several parallel subagents. Let them output the relevant code paths only. Using gpt-5.6-terra-high on Codex and sonnet on Claude. Merge results into a single per-task research file. This saves tokens and context window of the bigger model, and avoids re-researching after compactions and in subagents. 2. Use the smartest agent (Sol xhigh ultra / Fable max) for both planning and execution. Tell it to use the research file where possible. 3. Switch to dumber agents for verbose substeps, e.g. gpt-5.6-luna-medium / sonnet is enough to drive browser use. Sadly, got no numbers to back this approach. PS: Of course, different task complexities and codebase complexities demand different approaches. The most expensive part is actually the code review step, which they don't mention/have at all. We should come up with some sort of complexity grading, so that discussions like this can be contextualized.
- pmontra 2mo agoI've been doing more or less this (from the post) for months: > plan deeply, then capture the plan as a todo list, then start. I do the plan deeply phase by writing a 50 line specification myself and then asking Claude to evaluate it, find what I missed, ask questions. I answer the question and we iterate until I know that we have what I had in mind to do. Then I ask it to write the implementation plan, that I might end up reviewing because it always asks some more question. Finally it implements the steps. If the planning was good the implementation is fast.
- kimonsodu 2mo ago[flagged]
- 2mo ago
- leobg 2mo agoI guess Anthropic knows this. They show you this warning: --- Switch model? Your next response will be slower and use more tokens This conversation is cached for the current model. Switching to Opus 4.8 (1M context) means the full history gets re-read on your next message. --- Reminds me a bit of airline booking sites ("We noticed you didn't add insurance").
- unholiness 2mo agoI mean, this is real. Your KV cache lives for an hour since the last token (on anthropic pro/Max at least, today at least, this has degraded in the past). So once you pay, say, 50k input and 50k output Opus tokens, you don't pay for cache reads of the 100k context while it builds. Switch to another model, you'll start with the cost of 100k input tokens on the smaller model to get that context loaded and its unique KV cache set. The post may have some real insight here, where this 100k working context is actually better than trying to summarize it's findings into a plan. It's also right that the handoff is a perfect time to edit the context (removing planning instructions). But it doesn't mention it's a trade-off: the plan is smaller, so it's a cheaper "on-boarding" of the next model. Send quite plausible that this is worth it for 1-off tasks. If it's right, this is basically "plans are useless, planning is everything" for LLMs. My problem is, I think the plans are useful. I want to review and edit them. I want them to give context for the upcoming code review (even if humans aren't reviewing). LLMs are notoriously bad at explaining why they're doing something in the moment. Humans are notoriously bad at accepting there's no reason why. I think the humans have it right here, and to bridge this gap want my PRs, my docs, and my comments teeming with reasons why. (NB: ranted to long, reposting at top level)
- deleted 2mo ago[deleted]
- Incipient 2mo agoWait this is new? Large context models figure out the architecture of the request. Miss sized models plan out each requirement, and smaller models implement a tightly detailed task? Isn't this the standard approach?
- benrutter 2mo ago> Miss sized models plan out each requirement, and smaller models implement a tightly detailed task? Isn't this the standard approach? It is, but the article is suggesting something different. The central idea is to use /prewalk to load slightly edited context from the more expensive model into the one, so that the cheaper model doesn't burn tokens reading all the files again.
- ghiculescu 2mo ago> We upstreamed it to omp For those not sufficiently cracked... what is omp? Is there a way I can have this command or workflow in Cursor / Claude Code? I tried going to the homepage (https://stencil.so/ https://stencil.so/). It didn't really help.
- rootlocus 2mo agohttps://omp.sh/ https://omp.sh/ - It's maintained by the author of the article.
- anentropic 2mo ago[dead]
- snehesht 2mo agoOh my Pi - batteries included pi harness
- heisenbit 2mo agoI‘m really wondering about plan and then agent mode switch in Copilot even with the same model. The switch swaps the prompt so all the learnings during planning which are in the cache will get invalidated.
- deleted 2mo ago[deleted]
- kademolu 2mo agoGood stats but how does this work with models like Claude where a lot of the context or relevant information is stored in its "memory", other coding agents can't use it.
- CBLT 2mo agoMemory is a harness feature, not a model feature. The author is probably only evaluating their own harness with different models, so Claude-Code-specifics like memory aren't part of their evaluation.
- walthamstow 2mo agoYou turn auto memory off in Claude Code and manage the context yourself
- maelito 2mo agoThis first chart is very hard to read, in dark mode and thin lines and text.
- ursuscamp 2mo agoCan someone explain to me the difference between this approach and using planning with a larger model, then just switching to a small model for implementation without clearing the context? I understand that it specifically does the first edit as well either way the larger model. Is there some other difference I am missing here?
- actionfromafar 2mo agoIf I understand correctly, switching to a small model makes the small model read the context again.
- trash_cat 2mo agoWhen a frontier makes a succesfull edit based on the plan that it made, it leaves an procedural trace in turn biases the NEXT model, low cost model, straight into procedural action. The cheaper model doesn't need to reread everything again because it has enough information from the frontier model to complete the task. A simple "plan" of what needs to be done does not carry this information.
- unholiness 2mo agoNo mention of KV cache, one of the biggest reasons not to switch models mid-stream. Once you pay, say, 50k input and 50k output Opus 4.8 tokens, you don't pay token cost for cache reads of the 100k context while it builds. Switch to another model, you'll start with the cost of 100k input tokens on the smaller model to get that context loaded and its unique KV cache set. The post may have some real insight here, where this 100k working context is actually better than trying to summarize it's findings into a plan. It's also right that the handoff is a perfect time to edit the context (removing planning instructions). But it doesn't mention it's a trade-off: the plan is smaller, so it's a cheaper "on-boarding" of the next model. Seems quite plausible that this is worth it for 1-off tasks. If it's right, this is basically "plans are useless, planning is everything" for LLMs. My problem is, I think the plans are useful. I want to review and edit them. I want them to give context for the upcoming code review (even if humans aren't reviewing). LLMs are notoriously bad at explaining why they're doing something in the moment. Humans are notoriously bad at accepting there's no reason why. I think the humans have it right here, and to bridge this gap want my PRs, my docs, and my comments teeming with reasons why. Plans help with that.
- CBLT 2mo agoNot by name, sure, but it does mention that you're going to be reading the same context into the second model. It specifically refutes your point that you're only reading the smaller plan: > Opus reads base.py, signing.py, the test file (twenty cards of gray), then writes its plan and leaves. And what's the first thing Flash does with that beautiful document? It re-reads base.py and the test file, because a plan is not a file and you cannot edit prose. The gray reads just keep stacking, first at Opus prices, then again at Flash prices. There is no version of this where a second reader is the cost optimization.
- resonious 2mo agoThe article shows that it's still cheaper to pay for the switch than it is to let the frontier model do all the work. At least in benchmarks. If you want to interact with plans then I think this technique just isn't for you.
- pjerem 2mo agoI use GLM 5.2 for everything and it's dirt cheap anyway.
- robbie-c 2mo agoI'm begging people to write articles themselves rather than letting Claude do it for them. I want an expert opinion, if I just wanted to ask an LLM I have my own. How can this article not mention the KV cache even once?
- abirch 2mo agoI wish that Hacker News had the option to flag things at AI and let people filter those things out.
- jxmorris12 2mo agoMe too. maybe Pangram solves this?
- ninju 2mo agoDiscussed here https://news.ycombinator.com/item?id=48886741 https://news.ycombinator.com/item?id=48886741
- draw_down 2mo ago[dead]
- resonious 2mo agoWhy does KV cache matter if they show it's cheaper anyway?
- robbie-c 2mo agoWhile I'm here - I recently optimized our PR review skill to make better use of the KV cache. Previously, it loaded up the diff, persona and prompt, and wrote those into the first message for each of 6 sub-agent reviewers. The prompt was templated with the persona name, so was slightly different for each reviewer. My optimized version had a common first message with diff + prompt, and a script to run to atomically claim a persona. It also runs the first reviewer before the rest to warm the KV cache, and the agent doesn't launch the rest of the subagents until the first persona has been claimed (which means that the LLM is running, and therefore the KV cache is warm). The agent has a script it runs which does the waiting for it. Agents 2-6 only run once the KV cache is warm, and because the intro message including the diff is shared, it's there in the cache. Only the persona file is different. In my testing this brought down the cost of a review by ~half, though of course this depends on how big the diff is, how many agent are launched, and what model is used.