Agree, I put off using org-roam a few times in the past because I couldn’t be bothered to keep my notes in such order. But now I have an agent that set it up and it’s also recording, organizing, and referencing everything.
On a recent trip to SF I found Waymo to be cheaper than Uber about half the time. I wondered if I was seeing artificially discounted prices as a new rider.
Agree. I think they're intentionally sitting on the fence between "These models are the most useful" and "These models are the most dangerous".
They want the public and, in turn, regulators to fear the potential of AI so that those regulators will write laws limiting AI development. The laws would be crafted with input from the incumbents to enshrine/protect their moat. I believe they're angling for regulatory capture.
On the other hand, the models have to seem amazingly useful so that they're made out to be worth those risks and the fantastic investment they require.
At the end of last year I took the old VCR and VHS home movies from the 90s out of my mom's basement and digitized them using this cheap converter[0]. I also had to get some rubbing alcohol and foam swabs to clean the VCR heads, which only took a few minutes[1] (some of the older tapes would play normally for a moment before the quality degraded into poor tracking and static). My family enjoyed watching them.
> I've written tons of code, I've taken no shortcuts, and I've focused on improving over my many iterations. This has enabled me to be an effective steward of generative coding (etc) models, but will younger engineers ever get the reps necessary to get where I am? Are there other ways to get this knowledge and taste?
I think this will be a problem in the middle term, and I've written about such deskilling before [0]. With the latest crop of foundational coding models and harnesses, and more progress on the way, I'm beginning to wonder if it will matter? If there's a future where agents are designing the code, implementing the code, and reading and reviewing the code... At that point the code is no longer the thing. "Software engineers" will continue to sit at the interface of product and software, but the software will be writing itself. Of course there will be a need for programmers who can actually read and write computer code, the same way there's a need for Fortran and compiler devs today.
The skill that all software engineers will need to learn, regardless of level, is how to leverage commoditized reasoning to build products effectively.
- how to design systems declaratively and in terms of requirements and constraints
- how to configure the systems in such a way that they're automatically testable end-to-end
- how to move tacit knowledge out of people's heads and into the context (all of our meetings will be transcribed; questions from the agent will be generated during the meeting resolve ambiguity; the agent will be an omnipresent attendee in all meetings: "Agent: The topic you're discussing overlaps with what Sally said three days ago when she met with Mike. They covered xyz..."; companies that follow remote work best practices will have an advantage here)
- how to allocate and orchestrate teams of people and agents
thanks for sharing this, it resonates with me. as some other commenters have said, CLIs, cURL, and other shell tools are composable and discoverable. it seems like a good direction.
auth considerations are present in the design of MCP. this, as opposed to the hodgepodge auth story with CLIs. there are APIs that either don't support OAuth or where using bare credentials is more expedient, and using agent-visible env vars is a security incident waiting to happen. but that doesn't necessarily mean we must use MCP. i think it's a matter of time before agentic tools come bundled with a proxy layer from which secrets / env vars can be set and used but not directly read [0].
you may have issues running in Docker; when i last touched this i needed to modify docker.sh:
-docker run --rm -ti -v "$PWD":/usr/src/koans -w /usr/src/koans golang:1.6.0-alpine go test
+docker run --rm -ti -e GO111MODULE=off -v "$PWD":/usr/src/koans -w /usr/src/koans golang:1.18-alpine go test
I'm using mcp-agent and have tried the orchestrator workflow pattern[0]. For deep research I'm having mixed results. As far as I can tell, it's not using prompt caching[1] with Anthropic models, nor the gpt-5 responses API[2], which is preferable to the completions API. The many MCP tools from a handful of servers eat up a lot of context. It doesn't report progress, so it'll just spin for minutes at a time without meaningful indication. Mostly it has been high cost and high latency without great grounding in source facts. I like the interface overall, but some of the patterns and examples were convoluted. I'm aware that mcp-agent is being worked on, and I look forward to improvements.