Earlier quoted context omitted.
> When I was working we used to get requirements that literally said things like, "Get data and give it to the user". No definition of what data is, where its stored, or in what format to return it. We would then spend a significant amount of time with the product person trying to figure out what they really wanted. This is a big HN LLM discussion divide. I am in the same no-specs work background camp, and so the ide…
I’ve never understood engineers who complain about vague specs… if the spec was complete, it would be code and the job would be done already! Getting a 20 page spec delivered from upon high and mechanically translating it to code without any chance to send feedback up the chain sounds like… a compiler.
I don't think AI will make your processes go faster
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Re: I don't think AI will make your processes go faster
#92I actually have data on this. I’ve been building sharc, a Common Lisp port of Hacker News. https://www.github.com/shawwn/sharc If that sounds familiar, it’s because it’s what dang did over the course of several years. It’s taken a few weeks. I started right around May, and now it’s able to render large HN threads (900+ comments) within a factor of five of production HN performance. (Thank you to dang for giving actua…
I've had a handful of software projects in my career land essentially on the day I predicted, sometimes several months out, and the commonality across all of those projects was that the specification was crystal clear. Two of them were actual ports of an existing piece of software over to a new system. And so any time we had a question about the implementation, we could look at the existing version and immediately ha…
Re: I don't think AI will make your processes go faster
#93On the one hand, this is a clean post that explains exactly what a lot of us have been thinking and seeing on the job at large organizations doing tech work. Dear Author, I agree with you 110% and want everybody else to come to understand what you have written. On the other hand, it feels like we've been over this tens of times recently, on HN specifically and IRL at work. Another blog post isn't going to convince le…
I disagree, I think the visuals, Gantt charts, are precisely the kind of "PM speak" that can be understood. Sure it won't solve anything as long as C-suite and investors do innovation signaling but that itself can only last so long.
Humanity knows how to solve starvation. Clear routes were laid out long ago. The work is in adoption.
Re: I don't think AI will make your processes go faster
#94I think when LLMs first came out people thought they could just say something like, "Make a Facebook clone". But now we're realizing we need to be more exact with our requirements and define things better. That has always been the bottle neck in software. When I was working we used to get requirements that literally said things like, "Get data and give it to the user". No definition of what data is, where its stored,…
This has significantly helped devs and made sure that requirements are very clear.
Honestly, with the first step, it seems the PMs are already halfway there to implementation of the feature so I wonder if in the future they'll just do everything themselves and a few devs will be around as SDETs rather than full blown implementers.
Re: I don't think AI will make your processes go faster
#95I think there's an interesting dichotomy. I find that for things I'm already capable at, LLMs are relatively inconsequential. But for things I'm no good at, it's a huge game changer. For a large company, that's going to be able to hire out most needed roles for any given project, this means the overall effect is going to be relatively inconsequential. At best, they may be able to cut down on labor costs by having one…
> I find that for things I'm already capable at, LLMs are relatively inconsequential. But for things I'm no good at, it's a huge game changer. What are the chances that this is the Gell-Mann amnesia effect? Sounds like the textbook definition of it. Personally, I find the exact opposite to be true. LLMs only help me when I already know exactly what I'm doing.
To wit, the answer pre-AI was to hire an expert on that thing, and you would then critically assess their work product, despite being unable to build it yourself.
Re: I don't think AI will make your processes go faster
#96>Process blocked on human inputs
Have AI check chat, email, issue tracker and see who it's blocked on and what latest status is. It may not save a huge amount of time but it can dig through the info pretty quick.
>Exploration
Once again, have it scour issue tracker, chat, customer suggestions, product documentation and summarize history and current status. Much quicker than setting up new meetings to try to rediscover and organize existing info.
Another use case, have agent build prototype, hand to people, have AI summarize and integrate feedback.
Claude or ChatGPT + Slack MCP + Jira MCP + Google Docs MCP + internal knowledgebase MCP + gh (GitHub) CLI + Datadog MCP--really 1 MCP per process in the Gantt chart--has been a huge boost at work just digging through context scattered all over the place and summarizing.
That said, it definitely still needs supervision and hand holding along the way
Re: I don't think AI will make your processes go faster
#97Earlier quoted context omitted.
I’ve never understood engineers who complain about vague specs… if the spec was complete, it would be code and the job would be done already! Getting a 20 page spec delivered from upon high and mechanically translating it to code without any chance to send feedback up the chain sounds like… a compiler.
In my experience, the complaints are not about the specs and their vagueness. It's more about the political game to get them detailed. If you've not encountered the kind of organizational issues where getting an answer is like pulling teeth, you're kind of lucky.
Re: I don't think AI will make your processes go faster
#98I think many things that were true prior to AI are still true or more so today, but new workflows and processes altogether are needed. I suspect that comprehensive, detailed planning and specification documentation must be assembled in advance of beginning code (akin to waterfall) when working with AI agents. Furthermore, I still believe customers and other key stakeholders need to be involved early and often so that the product can iterate towards a better ultimate end state (i.e., agile). Unlike prior to AI, it's completely plausible to implement both types of approaches, and they aren't mutually exclusive. We can do comprehensive, exhaustive, thorough planning and specification documentation prior to handing off to dedicated engineering and products teams, AND we can work quickly and iteratively via sprints that aim for frequent meetings and updates with the stakeholders that matter.
I also think the same validation gates that mattered before -- linting, SASTs, but most importantly, comprehensive automated testing that gets run locally and in CI/CD and is regularly expanded to cover all expectations about the behavior and structure of newly-implemented functionality -- continue to matter now, more than ever.
New tools and processes also must be built to make human review, the single biggest bottleneck in software development today, more simplified and streamlined, and less taxing. I think tools like CodeRabbit and Qodo can help automate and expedite the code-review and approval processes, but they would be even better if they were working off more surgical and tiny edits. Bloated, verbose AI-generated code edits are the core problem here. Process management techniques to mitigate the problem of AI code overload can prohibit the submission of AI-generated PRs, require senior engineer approval of any PRs prior to merging, or block the maximum number of lines or changes made. More sophisticated processes like Graphite's stacking of PRs are genuinely helpful in breaking down massive PRs into smaller chunks.
Finally, precision-editing tools for AI coding assistants like HIC Mouse (full disclosure, my project) that move beyond the existing options available to AI agents of whole-file replacement or exact string-replacement to enable agents at the editing-tool layer to perform surgical, tiny changes that don't touch any unrelated content, giving agents specialized visibility, recovery, and next-step guidance mechanisms that safeguard AI workflows, can materially reduce AI code slop by alleviating burdens upstream of code reviewers, both automated and human.
The bottom line: Shipping secure, production-grade code was never easy and always took a long time. It's not necessarily easier now just because certain aspects to the overall process can be generated much more rapidly. Arguably, the hardest parts like human review and approval are much harder now -- not easier. Solutions will take hard work and must be tested in the crucible of real-world enterprise usage. I am guessing that companies that deploy successful processes will be wildly profitable. Those that don't, including well-established incumbents, will fail. I do think AI absolutely can give organizations a game-changing boost in development velocity of genuinely high-quality code that might even be better than anything ever created previously. I also fully agree with the author that for many organizations, AI will not make their processes go faster and may even slow things down.
Re: I don't think AI will make your processes go faster
#99I think when LLMs first came out people thought they could just say something like, "Make a Facebook clone". But now we're realizing we need to be more exact with our requirements and define things better. That has always been the bottle neck in software. When I was working we used to get requirements that literally said things like, "Get data and give it to the user". No definition of what data is, where its stored,…
In what I've seen, tickets are much richer in detail now because PMs are using AI (connected to the codebase itself, like Claude Code or Codex) to fill out a template as to what and why the problem is (ie X field exists in the backend not frontend), how and where to get any data (query the backend), and what acceptance criteria is needed (frontend should have the field exposed and "submit" should push the field's dat…
And then someone copy pastes it into Claude and now those inaccuracies become part of the code and tests.
Re: I don't think AI will make your processes go faster
#100I think when LLMs first came out people thought they could just say something like, "Make a Facebook clone". But now we're realizing we need to be more exact with our requirements and define things better. That has always been the bottle neck in software. When I was working we used to get requirements that literally said things like, "Get data and give it to the user". No definition of what data is, where its stored,…
It's worse. Vague requirements still only power vague interpretations of the problem. Even if you provide good requirements, you still only have vague interpretations at your fingertips. The promise is that such things won't be a problem in the future, which is obviously not materialising. "Make a facebook clone" is the vague human promise to the end user. The reality is that it leads to so many assumptions which are…
Anyone who thought that gap could be shrunk substantially lives in delululand.
Hence why we haven’t seen this explosion of ‘really great’ products come out.
Many will continue to parrot ‘bro but the models changed I swear’. I’m sure they did. But you’re missing the damn point.