CharlieDigital

The seniors shouldn't be building the product; the product team can build the product directly. There is no reason for a product manager to ask a senior engineer to fix the spacing or update the tab sequence for some workflow. The harness should be sufficient for them to do at least that. Get it right and a product manager should be able to build whole features if enough of the heuristics and system level constraints are in place.

I genuinely prefer OpenAI models for coding and building in general. My observation: GPT models are generally better at following the system prompt, tool use, and skill use.

I use Paseo and there is a noticeable difference when GPT-6.1 Sol is driving the orchestrator vs Opus/Sonnet 5.5 driving the orchestrator. Anthropic models ignore the shared system prompt to use the Paseo tools for sub-agents and prefer to use its own native sub-agents. It ignores the skills mentioned in the system prompt, but will use them if I explicitly tell it to in a user prompt or I extra emphasize the instruction in an XML fenced block in the system prompt or CLAUDE.md like <BOOTSTRAP_MUST_EXECUTE_AT_START>

Sometimes, I feel like I have to explicitly prompt Anthropic models to behave the way I want it to (even with system prompts in place with the EXACT instruction). Sometimes it even feels like it's disregarding CLAUDE.md. To me, this makes me feel like it is the less compliant of the two because I constantly have to steer it manually. Anthropic models also feels like they prefer to go lone-wolf. I want my orchestrator to use the other subs (configured with different platforms and models) to evaluate a design; Opus really wants to lone-wolf and solo everything.

That said, I prefer Anthropic models generally only when the task is more open ended and exploratory. The Anthropic models' "mind of their own" benefits here; GPT models tend to be too agreeable; and too eager to agree with any change in direction. But for actual coding once a direction/plan is in place, I would much rather delegate it to GPT (more likely to draw on the harness for context, skills, instructions, and tools -> more consistency).

Anthropic's 1m context window default and shorter cache TTL are also not great in agentic flows where an agent could become idle and then re-activated. Burns tokens very quickly. I have it tuned down to 300k across all models (recommend doing this for multi-agent coding).

The job of the best devs is to build the system ("the harness") that let's anyone build near their level of competency by unloading their heuristics into the harness. That is what the most senior, most talented devs on the team need to be doing so that the baseline gets lifted across the board.

A lot of accountants are old school and I'd guess that most folks outside of tech are not nearly as heavily on the AI train.

Additionally, I think the industry and individual practices are probably still figuring out contracts, accountability, and legality. Will their insurance policies cover mistakes made by an AI?

I'd also guess that its one thing for you to feed your financial documents to Claude; would you feel as comfortable if your accountant were feeding it to an unknown model of their choosing? I'm not so sure I'd be.

It can drive 3 displays using a DisplayLink dock/adapter.

Terrible idea because these two artifacts (ticket and code) have different lifecycles, different participants, different rates of churn, different needs from a reporting perspective, and different rules/workflows.

Tickets being external to source decouples the systems, parties, and lifecycles.

Difference is in the eventing model and complexity of state management in React's hooks based model vs vanilla's. WC is aligned with how JS callbacks naturally work. React is the only framework that points the callback to the component function.

My experience with Next.js is that because it mixes multiple paradigms, it makes using any single one of them more complicated and error prone.

Not just as the dev delivering the solution, but also library and package authors.

Ran into lots of issues around Next 12, Next 13 was very buggy, moved to Astro and never looked back.

Specifically Brotli2Shell

Feels like the goal was sensationalism.

I think also heavily depends on what you're doing.

If you're in a stable codebase with lots of best practices, patterns, controls (linters, analyzers, etc.), examples already established, then any model will do ~the same plus/minus since there's not much novelty.

If you're building ground up, entirely novel, no reference, I think results will vary with lower capability models.

For me, I do a mix, leaving implementation to Haiku/Luna/DeepSeek and research/feedback/critique to Opus/Sol/Astra/Fable

They've already established and accepted that level of trust with Microsoft and deemed it acceptable. In this particular case of Copilot vs OpenAI vs Anthropic, the question is whether OpenAI or Anthropic are higher trust than Microsoft when it comes to AI, data handling/retention, etc.