While there is some use to being able to visually scroll a log of what’s happened, the whole goal with this is to enable an LLM to assist you. Minowa’s systems come with a Model Context Protocol server, a tool that provides AI friendly access to your data. While this is good, it does provide some guardrails for the AI, more was needed.

So we just released NightWatch Skills, a package of methods for Claude Desktop developed alongside the MCP server, that are built to help you focus ON you, instead of some complex new piece of software. You just install the Skills, connect to the MCP server, and you can get the AI to perform common tasks. The first three include:

• Intake Interview - get the system configured for your needs.

• Episode Analysis Report - detailed examination of events in a small timeframe.

• Scored Sleep Signature - a daily analysis of your Garmin tracker sleep report.

Since these are just Skills, anyone using Claude Desktop can have the system create a new one for repetitive tasks. There will no doubt be many more of these in the repository as we continue to explore what’s possible.

As the lead developer I’m also the guinea pig in chief. And my chief life complaint is that I do not sleep like a normal adult. Here’s an excellent example of why Minowa is needed for those of us wrestling with complex issues. Garmin thinks I was asleep from midnight to 5:30 AM based on heart rate and activity, with one potty break around 0200. Thanks to the correlation capability I can tell that first two hours wasn’t restful, just look at the blue stress graph. And thanks to logging I know what supplements I took before bed, and which remedy I resorted to there right after the potty break.

Have you ever read an article about how having a pet is good for you? Here’s some warm, fluffy data to support that. The flat red heart rate line shows when I was asleep, minus a brief excursion to close the house up and feed my deskpet at sunrise.

Then at 7:14 AM she plants herself on me for 47 minutes. Heart rate drops, stress plummets, and this sets me up for some much needed deep sleep.

Conclusion:

This software has evolved in different directions several times in the year since its creation. We are headed for a niche that is difficult to support properly, and for which I have found no competing tools. The next big advance will be putting the document feature to work, and that should come as soon as Monday or Tuesday.

The discount is not as insane as it sounds, what is Anthropic doing here?

• Training the people who will be hired by big companies.

• Seeding startups that may take off and drive demand.

• Grabbing as much of the consumer space as they can.

But the circular dealing of the AI players was the first warning sign, and now with the economic hit due to the Hormuz closure, it’s clear something is going to happen. SOON.

If my access disappears in August, I will start manually reviewing the roughly 60,000 lines of code in the Minowa backend. I will take stock on how many competitors will simply implode without the subsidized access. And I will be looking to see what sort of inference is available for consumers, probably with a focus on Apple hardware.

We speak of bubbles “popping”, but that’s not quite right. Bubbles deflate, perhaps with an initial dramatic run on the stocks of the frontier labs, but there will be a contest to consolidate, to be the last one standing, and to keep at least some of that datacenter investment active. Nothing will be stable, but those who are nimble will find a way to surf the decline, instead of experiencing a discontinuity.

I do periodically poke at local models with my 16GB M1 Pro processor Mac, but that’s generally a futile pursuit. Last week I took the time to get Claude running using PrismML’s Bonsai ternary (1.5 bit) models. This was also futile on my hardware, but some of you might be working with more potent machines that could use this.

First: Obtain Models

All this shizz keeps the absolutely atrocious Hugging Face downloader from exhibiting performance appropriate for a 300 baud acoustic coupler.

export HF_HUB_DISABLE_PROGRESS_BARS=0

export HF_HUB_VERBOSE=1

export TQDM_MININTERVAL=0.1

export HF_HUB_DISABLE_XET=1

export HF_HUB_ENABLE_HF_TRANSFER=0

export HF_HUB_DOWNLOAD_TIMEOUT=300

export HF_HUB_DOWNLOAD_CHUNK_SIZE=1048576 # 1MB

export HF_HUB_MAX_WORKERS=4 # fewer threads

for m in 1.7B 4B 8B; do

time hf download prism-ml/Ternary-Bonsai-${m}-mlx-2bit --local-dir Ternary-Bonsai-${m}-mlx-2bit

done

Second: mlx_lx

I already had uv for Python so a simple install got the Apple specific mlx engine installed.

uv pip install mlx-lm

Then you need a launch script and this was enough to start.

mlx_lm.server \

--model /Users/cornholio/work/Bonsai/Ternary-Bonsai-4B-mlx-2bit \

--host 127.0.0.1 --port 11435 \

--max-tokens 32768 \

--temp 0.5 --top-p 0.85

To test it do this:

curl localhost:11435/v1/models

And this is a working result

{”object”: “list”, “data”: [{”id”: “/Users/cornholio/work/Bonsai/Ternary-Bonsai-4B-mlx-2bit”, “object”: “model”, “created”: 1784933535}]}%

Third: lite-llm

Gotta have a proxy to make this stuff work in the Anthropic kinda way.

uv tool install litellm

And then you need a router.yaml to make the model calls work.

model_list:

- model_name: bonsai

litellm_params:

model: openai//Users/cornholio/work/Bonsai/Ternary-Bonsai-4B-mlx-2bit

api_base: http://127.0.0.1:11435

api_key: dummy

use_chat_completions_api: true

- model_name: “*”

litellm_params:

model: openai//Users/cornholio/work/Bonsai/Ternary-Bonsai-4B-mlx-2bit

api_base: http://127.0.0.1:11435

api_key: dummy

use_chat_completions_api: true

Then you invoke the router.

litellm --config router.yaml --port 11436

Finally: Claude

Claude is just stupid persistent about doing what it thinks is best rather than why you tell it, so you gotta firmly harness it.

#!/usr/bin/env bash

export ANTHROPIC_BASE_URL=http://127.0.0.1:11436

export ANTHROPIC_API_KEY=dummy

export ANTHROPIC_AUTH_TOKEN=dummy

export ANTHROPIC_DEFAULT_OPUS_MODEL=bonsai

export ANTHROPIC_DEFAULT_SONNET_MODEL=bonsai

export ANTHROPIC_DEFAULT_HAIKU_MODEL=bonsai

export CLAUDE_CODE_ATTRIBUTION_HEADER=0

exec claude --bare bonsai

And once you’d done all this, you get something like the following

90 seconds to get that response … and despite turning everything off and having 10GB free when I started, it pushed my machine into swapping, and I got fed up after five minutes and closed the terminal app.

Conclusion:

I had already backed off the 8B model to 4B due to memory pressure. After finishing this article I tried again with 1.7B and got the same pitiful performance. Bare calls to the 1.7B model with curl return in about 800ms. The models are fast, the problem is tens of thousands of context tokens, over and over, even for simple Claude Code activity.

I would like to be ready for the day Anthropic charges only enterprise pricing for the enterprise grade service I use now. That readiness would seem to be something like an Nvidia RTX 6000 Pro - a 96GB GPU with 1.8T/sec memory bandwidth. Having six times the memory and nine times the ability to move stuff around is a whole different world from even the more capable Macs.

I’d be curious to hear from anyone with more current hardware who does this - what do you have? How does it behave for you?

I log everything about my health into a web interface. Every medication, every supplement, the vital statistics I can collect at home, and the system will load Apple HealthKit and Garmin Connect data. While curating the data is important, there isn’t a lot of value to looking at that timeline. It’s much better if I let Claude Desktop use a skill that I created to analyze what’s available.

Understand the starting point here - I suffer from an immune system condition called MCAS, which basically means I’m allergic to eating. Anything to do with food, coming or going, can trigger a full throttle response from mast cells all over my body. My blood pressure plummets, my heart rate zooms, and I drop out in an episode that looks a bit like narcolepsy type II. Troubleshooting this is like a systemic problem in a datacenter - lots of systems to watch over a long timeframe and you have to look for complex correlations.

This graph is my sleep Sunday night. I had previously noticed that taking choline+inositol, an anti-tinnitus preparation, dramatically reduced the Stress reported by my Garmin Vivoactive 5. When the band is reporting high stress, I am experiencing palpitations, an always annoying and sometimes uncomfortable feeling tied to my heartbeat, without the rapidity of beats associated with tachycardia.

I took four capsules with 250mg choline and 250mg inositol in each, and I took none of the other medication I’d normally take in the evening. I seldom get four hours of sleep at a stretch, so seven hours in one shot seems to be a confirmation that choline+inositol is the sleeping potion I have long sought.

This is the most recent episode that I’ve explored with the tool. Notice the richer reporting.

What you see here is a chart of events from between 10:00 AM to 3:00 PM today. I had eaten, that’s the most common trigger for an MCAS episode, and I decided to run an experiment.

The red line is heart rate and the blue line below it is Garmin’s Stress metric. The stuff at the bottom, starting from the bottom up, are the Garmin sleep scoring for the time I was down, then the next three are pharmacokinetic information on the two Stacks and one individual supplement I took. The icons sprinkled on the graph are blood pressure readings - purple triangles are my wrist cuff while laying down, gold dots are the arm cuff I keep on my desk.

This time I took three capsules of choline+inositol, then my usual Sleep Stack of six grams glycine, 600mg N-acetyl cysteine, and 400mg magnesium glycinate. As it might have been the start of an MCAS episode, I also took the Restless Stack, which contains my old friend kava kava, which I guarantee to knock down palpitations in about half an hour.

I was expecting to slide into a long, restful sleep, but that chart shows anything but that. You can see from the last purple triangle with a 102/62 label that I was awake for a while. I fell asleep when the Sleep Induction hit, but instead of relaxation there’s what looks to be a supplement interaction. The interaction wains, I do sleep, the Garmin reports me awake but there’s no logging, so that means I was just restless, and then the stress level begins to climb … yet I fall into deep sleep?

When I awoke from this I felt AWFUL. Not just palpitations, but all over body pulsing. I got out of bed and had to hold on to all the things to get to the bathroom - serious orthostatic hypotension. When I got to my desk I got a 96/70, which is down near MCAS crash territory. I piled on liquids, but I wasn’t dehydrated, and got a 115/65 and a 106/61.

I am debating what to do this evening, but I kinda think the choline+inositol and kava kava combo was the culprit here. I will probably try the Sleep Induction stack with choline+inositol as a companion, and if the stress curve overnight looks good, maybe the choline+inositol becomes a standing member of that stack.

Conclusion:

If I didn’t have the log and the Garmin working together, I’d have never spotted the link between Stress and choline+inositol. This is the second big clue on managing my condition in the last couple weeks - I had an appointment with my allergist a week ago Monday and had only just sorted out that I was reacting to L-theanine, a former member of the Restless Stack. This reaction had cost me more than half my work hours in the months of June and July, so getting that resolved was a Great Big Deal™. Had I gone in without having nailed that down I might have lost another quarter or two of my life. As it was, I got the adjustment in meds I had been seeking.

I am not a doctor.

What I have going for me are 1) a computer science education 2) experience running distributed systems where log correlation for troubleshooting is key and 3) the cognitive sciences discipline behind intelligence analysis.

I can make software do what I want, I can handle complex interlocking problems, and the skepticism that comes from having worked on complex analytical problems serves me well here. The Anthropic AI models offer facile explanations for everything. They’re wrong about half the time, even when they’ve been constrained. Having this sort of tool is just a start, you have to learn to use it properly, or it’ll lead you badly astray in short order.

This system has the following features in its current form:

• Support for multiple users on one system.

• Companion mobile client that will be released soon.

• MCP connector for use with Claude Desktop.

• Daily logging of medications and supplements.

• Import of Apple HealthKit and Garmin data.

The very next things coming will include:

• Personal document handling.

• URL library so AI is constrained by PubMed Central publications.

• Improved semantic search in the MCP connector.

For those of you who vibe code, extra effort was made for onboarding. The features in this area include:

• LSP Enforcement Kit - replaces text command line tools with AST aware options.

• Serena - Abstract Syntax Tree map of projects.

• CodeSight & OptiVault - AST tools that dramatically reduce token burn.

• Rigorously documented and maintained SQL data model.

• OpenAPI framework that is also rigorously maintained.

• Extensive multilayered testing discipline.

• Automatically installs in local Docker and remote Linux systems.

These guardrails have evolved over the last year of development work and they provide fairly good discipline when dealing with the lazy, impulsive LLMs that we use for the sake of rapid progress.

If this interests you the daily scrum is in r/MinowaHealth …

Hard to believe I wrote almost every day for twenty eight months when this thing first launched, isn’t it? This startup business is not for the weak …

I’ve never been happy with the branding for HealthyBuddy, nor the successor domain, and while I like Kanagami for uniqueness and protectability, it doesn’t work for the dyslexics among us. So the unique name we settled on for incorporation, Minowa, is the future.

Let’s get back to building the harness, now that we’ve got another shot at some introductory offers due to the name change. Today we’re looking at my least favorite topic, email.

Read more

We started a repeatedly renamed company last September - my LinkedIn says HealthBuddy.ai, the domain for production is seenwhole.ai, and the incorporation paperwork says Minowa. Inc. I purchased, but we could not use Kanagami.com - we have a couple people with dyslexia, and I got thorough roasted for this name.

Here, at the start of June, a reality check was needed. We didn’t move quickly enough to ride the AI leverage wave. I think we still have a viable bootstrap operation, and this is echoed by my cofounders, our small set of beta testers, and most importantly by more than one vendor in the healthcare space. There’s a burning need for what we’re doing, and despite all the activity in this area, no one else seems to be taking precisely the same course we chose.

So this week we’re taking inventory - we do we have that is done? What can be made ready in time for the start of Q3? There are a lot of vibe coded apps in the world, we’ve always been playing an enterprise/FOSS game - making something for a user is one thing, making something that an entire health system could adopt is many things, and none of them are within reach unless the builders have enterprise experience.

So if you’ve been wondering where I’ve been, that’s half of it. The other half has been a move to a lovely house that I’m sitting during probate. I don’t know exactly why, but I’ve been struggling with MCAS symptoms that were formerly pretty well controlled. Perhaps due to the shift from San Francisco climate to the California Delta, perhaps the reduction in walking due to there being much less to do here, or maybe it’s a side effect of the slow roll of prescription solutions, all of which seem to take a solid month to show any results.

But whatever the case, the only way out is through … so I’ll keep trudging.

• August 5th, 2025 - Claude Opus 4.1 released AND the day I began treatment for MCAS.

• September 29th, 2025 - Claude Sonnet 4.5 released

• November 24th, 2025 - Opus 4.5 released.

• February 5th, 2026 - the mighty Opus 4.6 released.

• February 7th, 2026 - 1st /insights

• February 17th, 2026 - Sonnet 4.6 released.

• March 22nd, 2026 - 2nd /insights

• April 16th, 2026 - Opus 4.7 stumbles into the sunlight.

• April 28th, 2026 - 3rd /insights

So I did some introspection after Opus 4.6 arrived, then against after Sonnet 4.6, and now again after Opus 4.7 has recovered from its initial troubles. The new Opus is the proximal change at Anthropic, but I have also recently set up second-opinion from the Trail of Bits skills. I’ve tried it using Claude wrapped in several Ollama cloud models, but DeepSeek-v4-pro, just yesterday, gave me some serious feedback about my big development project. I guess that’s why I’m in a thoughtful mood.

I don’t know that I have a lot more to say here - these are the summaries from three reports on my personal progress. I’m going to suggest that maybe you get in the monthly habit of asking Claude Code for your /insights, save them, and review your progress. I’m feeding these to Claude Cowork so I can update my growth plan.

I’m setting up some tooling to support hunting up potential paying customers and tracking interactions with them. I think there will be some insight to be had in this, as in one case it’s a company that’s ready to go to market, in the other it’s a founder who is just starting in a certain area.

Come on in and I’ll explain what I’m doing and why …

The health care startup, which has gone through a couple rounds of “we need a new name”, has two problems. The first is getting to the masses who have intractable health problems, and the second is finding and approaching their care providers. This other thing is similar in that it needs to gather information on licensed professionals who keep a public office, similar to the manner in which independent doctors do.

I have evaluated and tossed quite a few tools. The ones that stuck in this area include:

• Claude Cowork - now it has all the automation you can get with OpenClaw, and none of the security grief.

• Exa - this search utility offers an MCP server and it can do LinkedIn specific queries. Not all of the people we seek will keep a presence there, but it is common enough that it’s worth checking.

• Perplexity - we’re paying for Pro level service to get the web interface search functions and we might get a single seat of the $200/month Max at some point.

• Hubspot is a well regarded customer relations management (CRM) tool and they offer two seats for free, which fits both efforts.

What about all the things that I tried and abandoned? Staying on top of things in this market is like setting sail on an iceberg. Every once in a while the whole thing just rolls, and you’ve got to scramble to stay out of the water. What I am seeing with tools now is a tremendous time compression. Formerly, if you had a product with a similar price and a couple nice enhancements the market leader did not include, you could start carving out a niche. Now, thanks to the functionally instant shipping of new features, those attempted market penetrations are a LOT harder. It’s easy to make an innovation, but well nigh impossible to keep one.

Discards:

The debris field of discards over the last eight months includes no fewer than four team storage environments.

• We have Google Workspace but it kinda sucks for all the tools I want to use.

• We set up Jira, but getting people to move was … problematic, and things were not stable.

• We set up Wekan, avoiding cost, and we neatly avoided internal uptake, too.

• We got on the 90 day Notion promotion, and it works tolerably well, once you master the authentication shenanigans that come when you’ve got a personal and professional account on the same client.

So the most familiar, least capable incumbent remains incumbent, and the evals result in me doing a lot of setup work, and there’s just not enough activity to justify their continuation. So the solution to this conundrum is …

Trying Claude Teams. We need more AI capability and we’re getting three Max level accounts and two Pro. The thing you get with Teams is shared storage of Projects, which is NOT as capable as Notion, but if it’s attached to a thing we MUST use, and we’re paying for that thing, I think it’ll finally stick.

Wisdom: These tools I describe up above are decent. So are other market leaders. Some of the innovators may survive but I’m not sure they can thrive; it’s too easy to make off with the things that make them interesting in the first place. There ARE other tools that meet this criteria and if you or your team are familiar with one, assess how they’re doing in a world going AI. Microslop is foundering; big ships turn slowly. The smaller companies may have had false starts, but they WLL figure it out.

Change is a form of production, and as Nate B. Jones is fond of saying, “production is free, the taste to know what’s good is not”. You’re going to need to develop a sense of what is and is not possible in this world, both for your team, and the vendors you use.

Market Forces:

• Two out of every three planned AI data centers seem like they will not be built: political resistance from communities, the helium hit on chip production due to the Netanyahu/Trump (mis)adventure in Iran, the fact that power switching equipment lead times were up to five years, before a massive disruption in Asian economies due to the end of Persian Gulf natural gas flow.

• Even with the data center blockade, memory and storage prices are crazy. The GPU I got for $419 last fall now sells for around $700. The Nvidia RTX 5090s we offered to people who are doing certain contract jobs with the startup were under $3,000 when we made the agreements, and now they running $5,500.

• Google’s TurboQuant algorithm promises a four to sixfold reduction in KV cache memory required to run models without any reduction in their capability. This effect requires some pencil and paper math to figure out what it means on a GPU that you own, but for the hyperscalars it could mean a 400% boost in concurrent capacity just by changing code. This would take some of the heat off the datacenter construction push, if it works in production. Big IF.

• BonSai is now showing one bit LLMs that are performing as well as their sixteen bit counterparts(!) I don’t understand how this is possible, but you CAN download working examples. If this sticks we’ll see personal computing going wild with it. This means a 2GB Raspberry Pi may perform as well as my 16GB MacBook Pro. Next generation chips will include one bit instructions in their existing vector processing functions, which will yield astonishing on device capability. We’ll still need GPUs, but CPUs will be far more capable than they are today.

I know you want a nice “and this is what it all means”, but I’m not going to hazard a guess, not with a disastrous war in effect and a spectacular run of technological advances driving massive social change. Notice that things involving atoms are increasingly difficult, while things involving bits just keep getting easier.

I’m hoping the aforementioned combination of hard to buy/build, and whopper performance improvements using code that runs on existing hardware are going to balance each other, at least at the scale where I work.

A few final capacity/money related thoughts. Anthropic et. al. are massively subsidizing those of us using their service. I get Claude Max for $100/month. When I hit my limit, my little overflow fund drains at the rate of about $20/hour. I saw that I had used 400 hours of Claude Code(!) in the last 28 days, so the real world cost would seem to be around $50k/year per the two FTE equivalents I get out of that plan. These subsidies WILL end and that might come in a precipitous fashion given the world’s volatility. Are you ready to pay what the tokens you are using actually cost?

And an addendum to that. I spent an hour using a 24GB Nvidia RTX 4090 via an ssh tunnel last week, and I promptly put my 16GB RTX 5060Ti up for sale. Running a GLM model that required 19GB on disk was so dramatically different from anything I could do with the 16GB card, there’s just no point in hoarding a device that’s half of what I need. Assuming TurboQuant is smoothly integrated into tools like Ollama and vLLM, KV cache can be a fraction of its current size. A 32GB RTX 5090 will perform like a 48GB RTX 6000 Pro does today. This is a personal frontier for me - that’s enough of a card to do a lot of things I’m currently using Claude to do, and it’ll be nice having compute that does not have an enormous price tag attached to it.

Tooling Itself:

Now that we’ve got that 1,400 word preamble out of the way, let’s talk about actually wiring some stuff up for business.

• The minimum cost for Claude Team is five Pro accounts at $25/month each - $125 total. If you’ve got one person that’s really slick with AI, get them the $125/month Max, and your other four team members cost you $100/month total. So that’s $225 to have a well enabled group.

• You can use Exa from its web interface, but it’s served me better when it’s an MCP server that Cowork can use as part of its overall research process. You might have to open a text editor and touch a scary file that looks like this, but I am sure you can do it; Exa offers excellent documentation.

• Perplexity has always been better as a web interface than an MCP call in my experience, at least at the level of the gratis Pro account I’m running. This is no longer available as MCP, even if you paid for the $20/month Pro level service. I think Perplexity’s progression is a sign of what’s to come - the minimums are going to be $100 - $200/month for any sort of usable service. So if you didn’t get your gratis Pro last fall, shell out $20 to see how this works for you.

• Hubspot - there are a LOT of CRM systems out there and thanks to AI enabled acceleration many of them have excellent feature sets. This one was chosen by someone I work with based on their prior experience and it’s been fine. Thanks to Claude it was really easy to get a web enabled leads form running using a Cloudflare Worker, as opposed to exposing the site with some vibe coded thing I can’t properly secure.

To Be Continued:

This was written right around the start of April, now it’s the end of the month, and we’ve added a back injury to a cofounder, as well as two funerals this week for parents among our close circle of associates. I know 2026 is a global exercise in peak stupidity, but this .. pelting … can end any ol’ time.

This got really long before I even got to the Claude Teams setup, and I’m not even into that yet, because other things got in the way.

Now you know what I’m doing, the next time I write it’ll be about what we get out of having Claude Teams. I think we’ll be doing it at the $125 for five seats level, it’s just not clear to me that the compute value is gonna hold, and the founder of that other company prefers to minimize costs.

So until next week … ttfn.

It’s not nearly as bad as the last time, it’s just in the basement, so the HEPA filter was handling it, but yesterday I broke camp so I’m not under foot when the refurb crew arrives. There were other things in this place that needed attention, this pushed it to critical mass.

At the risk of some tiresome twerp doxing my hideout, I’m safe, and prepared for a Very Important Guest to join me for a bit.

What you see here is Cowork’s comparison between two conceptually similar tools - they use Abstract Syntax Tree to explore the logical structure of source code. This is just slop, but it’s slop in response to a computer scientist’s eye on the vexing problem of the moment, so maybe it’s worth a look if you’re trying to pick some tooling, but you don’t have the same education/experience I bring to the task.

-=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=-

Prompt: Here are repos for two different Abstract Syntax Tree based tools meant for use with Claude Code. Compare them and give me a broad ranging report on the differences, but try to keep it terse. Don't explain basic computer science concepts, focus on the advances.

Codesight vs. OptiVault: Comparative Analysis

Both are zero-dependency TypeScript CLI tools that compress codebases into compact representations for AI assistants, both offer MCP server modes, and both claim ~90-95% token reduction. The interesting differences are in what they optimize for.

Extraction Philosophy

Codesight is a semantic extractor — it understands what your code does. It identifies routes, ORM schemas, React components, middleware types, env vars, and library exports as first-class structured concepts. Its 8 parallel detectors each target a specific architectural concern and produce typed data (e.g., RouteInfo with method, path, params, tags like [auth, db, payment]). The output reads like an architecture document.

OptiVault is a structural compiler — it understands how your code is organized. It produces per-file “skeletons” containing dependency wikilinks and export signatures, plus a master RepoMap. It doesn’t know what a route or a schema model is; it knows that auth.ts exports verifyToken(token: string): Promise<boolean> and depends on database and crypto. The output reads like an index.

AST Strategy

Codesight dynamically loads the TypeScript compiler from the scanned project’s node_modules (three-level fallback: createRequire → direct path → pnpm store). This gives it real AST access for framework-specific deep extraction — walking NestJS decorator trees, resolving tRPC nested routers, chaining Drizzle field modifiers (.primaryKey().notNull() → pk, required flags). For Python and Go, it spawns lightweight subprocesses or uses structured regex with brace-depth tracking. Every result carries a confidence: "ast" | "regex" tag.

OptiVault never loads a compiler. Its entire AST layer is regex-based with brace-depth tracking for function body extraction. This is simpler and faster (no dependency on the scanned project having TypeScript installed), but it can only extract syntactic features — imports, exports, function signatures — not semantic features like route prefixes accumulated through middleware chains.

Scope of Understanding

That last row is significant. OptiVault’s read_function_code tool lets Claude request just one function’s source without reading the whole file — a capability Codesight doesn’t offer. And mtime caching means OptiVault’s watch mode patches individual RepoMap entries in ~20ms.

Output Model

Codesight generates a .codesight/ directory with domain-specific markdown files (routes.md, schema.md, components.md, etc.) plus a combined CODESIGHT.md. It also generates AI config files for multiple tools (CLAUDE.md, .cursorrules, copilot-instructions.md, AGENTS.md, codex.md). Its MCP server exposes 8 filtered query tools (e.g., get_routes filtered by prefix/tag/method).

OptiVault generates an _optivault/ directory as an Obsidian-compatible vault — each source file gets a corresponding .md note with TOON frontmatter (tgt, dep, exp fields) and a Signatures section. The master _RepoMap.md is a single-file index. Its MCP server exposes 4 tools, the most distinctive being sync_file_context which Claude is required to call after every file write (enforced via auto-generated CLAUDE.md).

Self-Enforcing Protocol

OptiVault’s most novel idea is the closed-loop protocol: it auto-generates a CLAUDE.md directive that forces Claude to (1) always consult the RepoMap before exploring, (2) read skeletons before full files, and (3) call sync_file_context after every write. This keeps the vault in sync as Claude edits code. Codesight has no equivalent write-back mechanism — it’s read-only context generation.

Language & Framework Coverage

Codesight is dramatically wider. It handles 25+ web frameworks for route detection, 8 ORMs for schema parsing, and 10 languages. OptiVault supports TypeScript/JavaScript and Python with a plugin interface for extension. OptiVault’s plugin system (LanguagePlugin) is cleaner for adding new languages, but Codesight ships with far more built-in coverage.

Testing

Codesight uses Node’s built-in test runner (27 tests, fixture-based with synthetic projects written to temp dirs). OptiVault uses Vitest (99 tests, heavily mocked with vi.mock()). OptiVault’s test coverage is substantially more granular.

Summary

Codesight answers: “What does this codebase do?” — routes, schemas, components, middleware, blast radius. Best for onboarding an AI to an unfamiliar project where architectural understanding matters.

OptiVault answers: “How is this codebase wired together, and what can I call?” — dependency graph, export signatures, function bodies on demand. Best for ongoing AI-assisted development where the vault stays in sync with edits and Claude navigates surgically rather than reading whole files.

They’re complementary more than competing. Codesight gives you the map; OptiVault gives you the GPS turn-by-turn.

-=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=--=@=-

Prompt: Are they safe to employ against the same repositories?

Short answer: yes, they’re safe to run together. There are no destructive conflicts.

Output directories don’t overlap — .codesight/ vs _optivault/. They’ll never touch each other’s artifacts.

CLAUDE.md is the only shared file, and both tools are append-only with independent idempotency guards. Codesight checks for the substring "codesight", OptiVault checks for an HTML comment sentinel <!-- optivault-protocol -->. Neither marker triggers the other’s guard, so both sections coexist cleanly. After one run each, both stabilize — re-runs are no-ops.

.gitignore is only modified by OptiVault (to exclude its vault dir). Codesight doesn’t touch it.

Runtime is also safe. Codesight’s AST layer loads the TypeScript compiler from the scanned project’s node_modules; OptiVault never loads a compiler at all. No shared state, no lock contention.

One thing to be aware of: if you run both as MCP servers simultaneously, Claude will see 12 tools (8 from Codesight + 4 from OptiVault). The generated CLAUDE.md will contain two protocol directives — Codesight’s architectural context plus OptiVault’s “always consult the RepoMap first / call sync_file_context after writes” protocol. These aren’t contradictory, but they do create competing attention demands on Claude’s context window. Whether Claude actually follows both protocols faithfully under that load is a prompt-engineering question more than a safety question.

Anyway, here is a tiny bit of Claude Code wisdom.

This is the result of me noticing how badly Claude Code has been acting, then finding a senior AMD person roasting them for declining performance.

CodeSight:

This one is just stone simple - drop this tool in and it automagically parses your project, adds itself to the CLAUDE.md file, and hooks so it auto updates. And so you don’t furrow your brow later, this one is complimentary to the other things I mention below.

CodeSight

Just Like .gitignore

I just learned that Claude Code respects .claudeignore - you put in the file types you want to not waste tokens on, and then you don’t …

# .claudeignore - Prevents Claude Code from scanning specific files/directories

# -------------------------

# Secrets & Credentials (CRITICAL)

# -------------------------

.env

.env.*

*.pem

*.key

secrets/

credentials.json

# -------------------------

# Python & Flask Specifics

# -------------------------

__pycache__/

*.py[cod]

*$py.class

instance/ # Flask instance folder (often contains local configs/DBs)

.pytest_cache/

.ruff_cache/

htmlcov/

.coverage

*.egg-info/

build/

dist/

# -------------------------

# Virtual Environments

# -------------------------

venv/

.venv/

env/

pip-wheel-metadata/

# -------------------------

# Database & Infrastructure

# -------------------------

*.sqlite3

*.db

pgdata/ # Local PostgreSQL/Timescale data volumes

vector_data/

logs/

*.log

# -------------------------

# Frontend / UI Dependencies

# -------------------------

node_modules/ # Token-heavy, block if serving GraphQL UIs or frontend assets

package-lock.json

# -------------------------

# IDE & OS Files

# -------------------------

.vscode/

.idea/

.DS_Store

Serena:

Next, stop burning so heckin’ many tokens with common tools that are not really up to the task. Instead of groping around with grep, put in Language Server Protocol. Given that I have JavaScript/TypeScript and Python, I picked Serena. This is an MCP server that will use command line tools to extract programming language features.

Once that’s running, you install the LSP Enforcement Kit. I’m not sure I believe the stats - 90%+ reductions in token usage? Today is my first time using it …

Degradation Workaround:

Having saved a bunch of tokens by outlining projects with CodeSight, not wasting time/tokens by using LSP Enforcement Kit, and employing the .claudeignore file to ensure that random cruft doesn’t end up in context, I’m being more efficient, but Claude is still dragging its behind all over my codebase.

So the next step is the Degradation Workaround. This collection of settings attempts to put Claude Code back to its fabulous early spring settings. These include

1. Stop-Phrase Guard Hook - notice when Claude is attempting to slack off.

2. Auto-Compact Window - clean house at 400k, don’t wait to hit the 800k wall.

3. Show Thinking Summaries - keep an eye on Claude to ensure it’s working.

4. Always Thinking Enabled - because Claude is weary, and tries to turn this off.

5. Effort Level High - they lowered the default to medium.

Here’s the JSON that’s required, but you really should read the article, I found it well worth fiddling with translate.

{
  “env”: {
    “CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING”: “1”
  },
  “hooks”: {
    “Stop”: [
      {
        “hooks”: [
          {
            “type”: “command”,
            “command”: “$HOME/.claude/hooks/stop-phrase-guard.sh”,
            “timeout”: 5,
            “statusMessage”: “Checking for lazy behavior...”
          }
        ]
      }
    ]
  },
  “effortLevel”: “high”,
  “autoCompactWindow”: 400000,
  “showThinkingSummaries”: true,
  “alwaysThinkingEnabled”: true
}

Conclusion:

And as you can see, I kinda need to conserve … 24% of weekly limit in less than 24 hours? Yikes …

First, we are In A World Moving On. I have a lot of very mixed feelings about where my life, our country, and our species in general are headed. But that’s in the past, so much so that I am startled by the distance when I do stop to consider what once was.

Second, it’s just not in my nature to hold grudges. I keep records, certainly, I’m famous for that, but I don’t cling to what’s in them. People get stressed, they drop out, or spin out, or otherwise make mistakes. Character still counts, that hasn’t changed, but as for that ancient history? shrug …

Third, having been almost magically healed by sharing my symptoms with an endlessly patient AI, rather than an overworked cog in the health care billing systems, I find myself restored to something approximating good health. Having physically turned the clock back twenty years, I am trying to do the same mentally and spiritually. I had some success in that world of endless conflict, but I do not care at all for the person I became while so engaged.

So there you have it. New look, new duties, and a new attitude. If you’re avoiding reaching out over something that happened back in the teens, it’s probably good for laughs at this point.

On the other hand, the purchase price of this card would cover a month of Claude Team for the company. The first person with $525 gets it shipped ground so the four of us can have Max and we get one Pro account for testing.

The release of Anthropic’s Opus 4.6 model on February 5th was an inflection point. Ask me years from now and I’ll be able to describe the before and after conditions. There have been a bunch of experiments in the seven weeks since, but the weekend of March 27th an agentic system … just emerged from what I was doing.

I’m going to describe what happened and give enough details that you can do something similar in your environment.

Attention Conservation Notice:

This contains complex integration and software development. Even so, a nonprogrammer just using the graphical interface to Claude and related tools will benefit from applying this thinking to their work environment.

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Qatar’s Ras Laffan helium plant was not so lucky. A third of the global supply is now offline due to a missile strike. There is a lot of concern, but nobody can predict what will happen, or when. There are simply too many moving pieces to this, and some of the ones on this map are inclined to shoot at each other. That’s Ras Laffan in the red bullseye in the northeast corner of Qatar.

If you want all the ugly details Nate B. Jones took a swing at it.

Show your work.

taps pencil …

OK, here’s what ***I*** mean when I say this … prior to viewing this.

An agent is a piece of software that I can give tasks in plain English and the expect that it will work for somewhere between seconds to many minutes, and come back with a complex structured result, which might be code, or English, or some more complex digital artifact.

I wrote that about a third of the way through this video from Nate B. Jones.

Here are the species Nate sees:

• Coding Harnesses — the simplest type, where an agent acts as a stand-in developer (e.g., Claude Code, Codex). Can be single-threaded (one developer managing tasks) or project-scale, like Cursor’s planner-executor model where an agent manages sub-agents. The key unlock is good task decomposition.

• Dark factories — fully autonomous pipelines where humans are only involved at the edges (writing the spec and reviewing the output). Agents iterate against evaluations/tests until the software passes. Most enterprises still have a human review step before production.

• Auto research — descended from classical ML, this is about optimizing a metric rather than building software. Examples include Toby Lütke optimizing Shopify’s Liquid framework and Karpathy’s auto-research for LLM tuning. If your problem is “metric-shaped” rather than “software-shaped,” this is the right tool.

• Orchestration frameworks — multiple specialized agents with defined roles handing off work to each other (e.g., LangGraph, CrewAI). Powerful for high-volume workflows like customer support tickets, but the coordination overhead means it’s only worth it at scale.

Having this list in front of me, I see that I have …

• A multifaceted Coding Harness

• Rudimentary Dark Factory constructs starting to appear

• The tooling for Auto Research is beginning with Grafana and PostHog

• The notion of an Orchestration Framework just arrived in the form of RabbitMQ and NanoClaw instances managing systems

As a rule for nontechnical professionals in 2026, I think you MUST have a harness, and that’s probably going to be Claude Cowork or maybe Perplexity Computer, if you’re REALLY research focused. I also think you SHOULD have something going in one of those other three areas. Take the time you win back from using your harness and try to make automation run longer, or deeper, or broader in one of the other areas.

McKinsey & Company has 40,000 human employees and 25,000 agents. Repetitive knowledge work is getting the same treatment that repetitive manufacturing work has been receiving for centuries. Thanks to Claude Code’s /insights command I know that I kept mine busy for just over 400 hours in the last four weeks. There’s no way to measure Cowork/Perplexity but I think I’m about half as busy there. That’s two full time developers and one full time research analyst.

Spend some time over the next four weeks paying attention to your AI utilization. See if you can get some information out of the system without doing any extra work, like I can with Claude Code’s /insights. Even if you can do that, I bet there are a lot of things that are NOT tracked. Maybe now is the time to get out your trusty pad and pencil and make this a serious introspection task.

You should be angling to amplify yourself; expect to have a copy of you, age 23, full of boundless energy, but unseasoned, doing things where you have expertise, and another you, maybe a bit older, working into new areas, and being a bit more careful as things progress. This should possible even with the entry level $20/mo Claude Pro.

And today I think we have a winner, in the form of NanoClaw.

Come on in and I’ll walk you through how to put a $20/month Claude Pro account in charge of a slick little agent setup.

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• How will humans working on their health employ agents to do the drudgery so they can focus on the high value learning and decision making tasks?

• How can the junior support person tasks that arise when using PostHog to track customer experience be turned over to an agent system?

• How can I take the junior support person duties found in operating Grafana, limited only to observation and escalation, creating an agentic system to shield the 24x7 support staff from alert fatigue.

I’ve been poking at the edges of this problem for a while, but on Monday I decided to solve it in an organized, systematic fashion. Come on in and see what I did …

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