[HN Gopher] LLM coding workflow going into 2026
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       LLM coding workflow going into 2026
        
       Author : lobo_tuerto
       Score  : 3 points
       Date   : 2026-01-10 21:30 UTC (1 hours ago)
        
 (HTM) web link (medium.com)
 (TXT) w3m dump (medium.com)
        
       | sciences44 wrote:
       | Really interesting workflow, Addy! Two questions about your
       | approach:
       | 
       | 1. *Spec - Implementation process:* How do you ensure Claude
       | actually completes everything in the spec and finds the optimal
       | path? Do you: - Document every step in extreme detail upfront
       | (spec as single source of truth)? - Use an agentic framework that
       | lets Claude self-guide through implementation? - Iteratively
       | validate each step with human checkpoints?
       | 
       | 2. *Tool comparison:* Have you experimented with GitHub Copilot
       | vs Claude Code vs Cursor? What made you settle on your current
       | stack?
       | 
       | I'm working on multi-step AI pipelines (3D mesh generation with
       | validation stages), and I find that LLMs often skip edge cases or
       | take suboptimal paths when given too much autonomy.
       | 
       | Curious if you've built any scaffolding/guardrails to keep the
       | LLM on track, or if your spec writing has evolved to be more
       | "agent-friendly"?
       | 
       | The balance between human specification vs. agent autonomy seems
       | like the key challenge going into 2026 especially to allow code
       | in production from agents.
        
       | dchuk wrote:
       | I've been using Agent OS (https://buildermethods.com/agent-os)
       | with Claude code's $100 plan and it is pretty darn great. I
       | usually ideate with Claude and ChatGPT back and forth on an idea
       | to get to a prd for the whole product/project idea, with a high
       | level roadmap, then go through agent os to bootstrap the core
       | artifacts and then it's just a loop of shaping specs, break into
       | tasks, implement, then I manually test it out. Using the
       | "standards" concept to produce skills in CC seems to help a lot.
       | I'm currently working on a Mac SwiftUI app (a language I've never
       | built anything in) and it's progressing nicely, has good test
       | coverage, and I haven't looked at a line of code. I found a
       | couple SwiftUI skills repos online, had Claude adapt them to the
       | agent os approach, and then hit the ground running. Also, it
       | basically functions like the really popular Ralph wiggum concept
       | everyone is raving about lately. Implementation basically just
       | runs on its own with a bunch of parallel agents, sometimes I have
       | to nudge the model after my smoke testing to clean up some stuff.
       | But overall, it just works, and is immensely productive. And this
       | agent os thing adds just enough structure to tame complexity and
       | variability. I highly recommend it and have no other connection
       | to it. I have some thoughts on some enhancements to it I'll
       | probably issue a few PRs for or fork the repo and implement.
        
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       (page generated 2026-01-10 23:01 UTC)