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 …

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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.

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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.

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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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While I am no longer in the security business, it doesn’t mean I’ve gone blind. There are terrible things happening in the iPhone ecosystem and you must update at once.

You should probably also enable Lockdown mode - it’s a PITA, but less so than losing control of your device. Go into Settings, put “Lockdown” in search, and it’ll take you right to it.

Seriously, don’t wait until tonight, you’ll forget, and the consequences are … shudder.

Here he’s managed to get his hands on a Tiiny.ai Pocket Sized AI Supercomputer.

The details, courtesy of Perplexity, are as follows:

• CPU: 12‑core ARMv9.2 processor.

• AI compute: Custom heterogeneous SoC + dedicated NPU, around 160–190 TOPS of AI throughput depending on source.

• Memory/storage: 80 GB LPDDR5X (with ~48 GB mapped directly to the NPU) plus a 1 TB NVMe SSD.

• Power envelope: About 30 W chip TDP and up to 65 W system power draw.

• Form factor: Pocket mini‑PC, Guinness‑style “smallest mini PC that can run 100B+ LLM locally” marketing claim.

So this is a potent ARM based system with a slightly puzzling 32GB of shared memory and 48GB of ram dedicated to the NPU. It compares very favorably to the AMD 395+ AI Max systems that start around $2,300 for a 96GB unit.

Assessment:

I don’t know that anyone reading this Substack is going to buy a Kickstarter funded specialty device. This is appearing here because it’s redefining the envelope - instead of $2,300 to get GPT-OSS-120B running at home, now it’s $1,300.

If one of these things were dropped on my desk this morning, it would likely end up running an 8 bit quantized MedGemma-27B. I could put seven of these devices in a 1U rack tray for the price of ONE Nvidia RTX 6000 Pro. They aren’t very fast, but a broad, slow way to do a lot of medical inference is potentially of interest to me.

We ARE going to end up with inference in our homes and offices that we control, inference that’s going to be dramatically more capable than GPU based solutions, because it won’t BE a GPU, it’ll be dedicated to just one type of task.

Claude Desktop is my office environment and research assistant, where I use Cowork to automate exploration I used to do by hand. This facet of the system recently got the ability to Dispatch work, which is Anthropic taking a swing at OpenClaw’s use. Earlier today I started seeing coverage about Projects, which have long existed for Chat, finally appearing in Cowork, too. Now you can make logical bundles of your activity so you’re not counting on some mutable context window to “remember” everything for you.

Once you have a Project in Cowork it’s not just your files, you can add specific skills, a set of connectors, or the curiously named “plugins”. Here’s a peek into what runs on my system - Notion+Slack for business stuff, Claude in Chrome and Exa are for research and testing, and both of my software projects (Healthy Buddy and Parabeagle) come with MCP servers.

The plugins are really interesting. This is a marketplace for … if you combine one of these with dispatch, it’s an agentic environment that will tackle a certain portion of your business.

I just installed the very first one to see what’s in there, and it’s a bundle of Skills and Agents.

Conclusion:

The big win yesterday for the startup was that we got PostHog running. I had only recently got the PLG Stack (Prometheus, Loki & Grafana) installed, which gives me visibility into the backend of our system. PostHog is similar in spirit, it’s a framework for monitoring user experience. You add a bit of tooling to your apps, both mobile and desktop, and the system will lets you keep a close watch on how things are working.

So we’re doing things a bit backwards - the most technical thing first, then where customers and technology meet, and finally I think we’re about to go deeper into business matters by employing Dispatch and Projects. We have some of this done using Notion for storage and Slack for the interactions, but it’s NOT been heavily used. There are a variety of reasons for keeping Notion, no matter what Anthropic offers at the team level, among them the fact that we’re moving fast on the concept that an agent is a thing that lives in a Linux VM we own.

What does this mean for you? If you are not yet using a paid Claude subscription, with the $20/month Pro being the least cost option, you need to go create that account right now. If you’re waiting for things to settle down before you invest time and money you are making the choice to be left behind.

So I just gave the whole thing to Claude Code …

Attention Conservation Notice:

If you’re not up to running a cloud computing provider of your own, this is going to be pretty dense. If you’re comfortable setting up Linux boxes at the level of doing stuff on command line, you ARE ready to have your own cloud computing setup … with the assistance of Claude and Perplexity.

System:

The machine in question is an HP EliteDesk G5 - a quad core i7-9700T with 32GB of ram. It’s got a 500GB NVMe drive, a single Western Digital RED 1TB NAS drive, and a pair of 500GB Apple drives that came out of a box of stuff I picked up at a flea market. The 2.5” are the smallest, but they’re also the quietest …

These are the storage pools as I built them prior to installed Claude Code. Snappy is a slice of the NVMe drive for whatever has to be really fast, transient is the curiously named 1TB of long term storage, and junkyard is the mirrored pair of 500GB.

This was the machine’s condition when Claude Code was installed. VM 100 named near contains Near.ai’s IronClaw, which I wrote about recently. VM 103 important is an old experiment that I’ve told the system to preserve. We’ll see how it does. The VM 601 template dockerbase is meant for turning up 8GB Docker systems on short notice.

Here’s the conversation with Claude Code right after I logged it in on the system.

Conclusion:

I have consistently used 60% of my tokens from my Claude Max subscription. I’ve accumulated a $60 “overuse” buffer in the account, which I’ve never managed to trigger. I want to put that additional capacity to work.

Development around here happens via Antigravity’s Claude Code extension and that system manages entire installs on both the local VM and the pilot VPS. There are an amazing number of Docker containers and the system just handles it, I long ago stopped needing to manually adjust stuff in that area.

What I think this new configuration is going to do is systematically evaluate things like IronClaw. I want to be able to give my “assistant” a GitHub repo URL and have it provision a VM, install the system, then maybe create some automated testing, depending on what the appliance in question is actually meant to do.

So what about you? The hard requirements I would set for a little Proxmox system are 32GB of ram and some sort of NVMe or SATA SSD. Machines themselves can be had for less than $100, see below. Storage prices have spiked - a 120GB SSD is going to cost nearly $40 these days and that’s plenty. You use 80GB for the base install and the rest of ZFS cache. Whatever random large drive you have will feel as fast as an SSD.

Couple this with a $20/month Claude Pro account and you can have Claude Code running in the Proxmox root, while Cowork runs the web admin interface via Claude in Chrome. You can watch what both are doing, reading up on things as you go. There’s basically no barrier to entry for a cloud computing setup of your own in 2026.

BLUF: If you’re a professional looking for career advice, get yourself Claude Pro so you can check out Code and Cowork, and start envisioning having a three digit monthly bill involving Claude Max. AI costs like turn of the century cell phone service, so get used to it. Unless you have job duties that involve buying AI inference at scale, there’s little point in trying to run it on your own.

Attention Conservation Notice:

There will be lots of numbers and hardware architecture stuff and it’s not going to be all that well organized. The above BLUF is all you need to know, unless you’re trying to build an AI based solution for your employer, or starting an AI focused company.

Services:

The frontier models - Google’s Gemini, Anthropic’s Opus/Sonnet, and the ChatGPT5 melange all run on VERY capable hardware. An Nvidia DGX H200 rack mount system has eight GPUs in it and they have 141GB of memory each. A million tokens - that’s 800,000 words - will take between 50GB and 300GB of memory, on top of whatever the model itself requires. That’s a bottom of the barrel box, there was a recent announcement about Anthropic agreeing to using Amazon Trainium chips on the input end and the dinner plate sized Cerebras processors on the output side.

You can not begin to compete with what a frontier model does by purchasing your own equipment.

Motivation:

I have reason to run models locally. The specifics include:

• Bulk embedding of documents and chats.

• Benchmarking and performance testing medical specific models.

• Understanding hybrid in-house/provider inference routing.

• Simulating mobile device inference performance.

• Exploring self hosted model security issues.

• Tuning models for my specific workloads.

As you can see, I need to understand how things I’ll be buying in bulk will behave, I need to touch models that don’t exist as services, and perhaps create “fine tunes” of my own.

Hardware:

Today I’m pecking away on an M1 Mac Pro with a system that has a single Nvidia RTX 5060Ti under my desk. Once we close on funding, I could see at least some of the following happening.

• Old Mac gives way to 32GB M4 or M5 Mac Air.

• RTX 5060Ti 16GB in my Proxmox workstation is joined by a twin.

• AMD Max+ 395 128GB desktop joins the mix.

• Nvidia DGX Spark 128GB replaces my Proxmox desktop.

• Mac Studio /w 128GB or 256GB for exploration/training.

• Nvidia RTX 6000 Pro 96GB GPU joins RTX 5060 in Proxmox workstation.

The only thing wrong with my current Mac is that there’s someone I know whose existing Mac is at death’s door. I am going to do something that lets me run larger models here at home. In order by increasing cost the Max+ 395 128GB ($2200), DGX Spark 128GB ($4500), Mac Studio ($4000 - $10000 depending), or the RTX 6000 Pro ($9200) are all possibilities. The 128GB unified memory systems are bringing the same amount of space as the dedicated 96GB GPU.

There is a distinct possibility that, even if I have plenty of money, only the first three get done. That would get me visibility into the three major AI ecosystems (AMD, Apple, Nvidia) for the price of just one of the three more expensive options. I cringe a bit to say this, I love owning my own gear, but the capital cost just doesn’t make sense. The $9200 acquisition cost of the RTX 6000 Pro must be compared against the roughly $3/hour cost of renting a dramatically more capable H200.

Conclusion:

Last fall I had a dying HP Z420 with an archaic 6GB Nvidia GTX 1060. The $500 HP Z4 that replaced it has been a good investment, steadily earning its keep. The $500 RTX 5060Ti? It’s satisfied my curiosity but it’s sat idle 99% of the time. With the benefit of hindsight, an RTX 3050 would have done that for half the cost. Buying a second 5060Ti would be very specifically for satisfying my curiosity about vLLM.

I suspect that when I have 96GB of GPU memory it’ll end up running Qwem3.5-122B-A10B. This is a 122 billion parameter model using just 10 billion mix of experts parameters at a time. I’d put it in an agent setup like IronClaw and its job would be standing watch over our production system.

But like I said at the start, unless you are an intense tinkerer, or your work duties involve understanding things at a volume purchaser level, let your AI spend be monthly services that get you access to frontier models. Services I see in my future would include:

• Claude Max - perhaps $200/month level.

• Perplexity Max at $200/month for at least a month.

• Perplexity Pro at $20/month if Max doesn’t get used enough.

• Exa - best business intel search I’ve used thus far.

• Minimal Gemini & ChatGPT just so I stay familiar.

• Maybe an API token spend for an agent, rather than local GPU.

Right now I’ve got what looks like a turn of the century cell phone bill. I expect it to increase to a turn of the century car payment - several hundred monthly.

If you are not running this hard, if you’re not “self-disrupting” in order to stay on top of things, 2026 is the year you’ll get hit from the shadows by someone like me who’s been eyeing your revenue streams.

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Let’s talk about how to fully exercise agents without using your entire life as table stakes for the game.

Problem:

You are really curious about agent things, but rightly cautious, and want a low risk way to test drive one of them.

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First, let’s put Claude Code in context.

The other day we had the helpful chart that covered Claude Chat, Cowork, and Projects. There’s no mention of Claude Code at all. Let me explain where it fits.

Claude Chat, Cowork, and Projects are all things that run in your browser, or in the graphical Claude Desktop. Claude Code is a programmer’s tool, it mostly runs in terminal windows. If you are not a programmer and/or don’t know much about using the terminal, just know that Cowork was created to bring the benefits of Claude Code to your experience. You can have a successful career without ever touching Code, but you MUST understand what people mean when they mention it, because you will likely be working with others who do use it.

That being said …

There are a couple of sources I lean on heavily as I adapt to this brave new world. First, foremost, and often mentioned is Nate B. Jones. If you want to know what’s happening next, this guy’s crystal ball is roughly as tall as he is. I watch every single thing he does, often rewatch (50%?), and at least a couple times a week I have Perplexity make a transcript so I can pull out an important section to show others.

That’s the strategy front covered, but there are smaller, simpler problems … which I presume is what brought you here. Mostly we’re focusing on the needs of those of you who are a “scout” or a member of a “strike team” — individuals and the related small groups who understand how to use AI effectively — the “AI natives”. Those terms are concepts are from a recent Nate video — 45 People, $200M Revenue.

I have my own set of sources that help me at this “closer to the action” level, and I really like physicist turned Claude Code guru Ray Amjad. Every time there’s a new Claude feature he will 1) explain it 2) demonstrate it in action and 3) produce some nice cartoon/diagram infographics to make it memorable. If you are starting to use Code, this guy is a GREAT source.

My Setup:

This is what happened to be on my desktop when I watched that video. I use Claude Code, but I put it in Google’s Antigravity IDE. What you see here is “HBbackend”, a GitHub repository that contains my company’s server side software. Using Antigravity lets me task switch between building this, helping our mobile developer review his code, or furthering my Parabeagle package when I’ve got a minute free. Antigravity holds what I was doing, so I’m not spending an hour trying to get back up to speed if I switch tasks.

The green area is the action bar - those are various functions in Antigravity. The yellow circled Explorer is the file explorer - just like in your documents folder, but it gets used in a GitHub repository. The folders and files you see are version controlled, this is how programmers handle complex, long running programming tasks.

The orange circled TEMPO-PLAN.md is a file name and the orange Claude icon next to it launches the Claude Code extension. The far right pane “We are using Grafana” is me having a talk with CC about building our application stack monitoring.

As a comparison for you non-programmers, here is what was going on in my Claude Desktop’s Cowork tab at the same time.

The left third of the screen are the conversations I’ve had with Cowork. The conversations you see are mostly server side development related. The right panel shows the resources being used in this Cowork session. The center is me talking with Claude, in this instance trying to get Cowork to read Slack templates - that’s me working on “TeamBrain”, the name for the “harness” we have for our “strike team” collective work.

You can see Chat, Cowork, and Code … where are the Projects mentioned in that infographic from the other day? They are NOT HERE YET.

This is an evolving environment, that panel on the right mentions the working folder, which is literally just a folder on your system. Projects serve the same role for Chat, but it’s purely internal to Claude Desktop, it can’t touch your system, it only gets what it is explicitly given.

Cowork is brand new, just two months old the day this article came out. Anthropic is bringing Claude Code type functions to non-programmers, which means they intend to drink Microsoft Office’s milkshake. This interface has changed three times in two months and it will keep evolving as they figure out what people actually need when they’ve got a top notch LLM to assist them.

Conclusion:

I do not yet have the YouTube equivalent of Ray Amjad for those of you who are excited to use Cowork. What I am doing here in terms of Problem/Solution will be bridging the world of Claude Code and Cowork, speaking to the “scouts” and whomever does the tooling for your “strike team”. I am not sure I have it in me to produce as slickly as Ray does, and I’m not sure this is even the right way to do things for the intended audience. My thinking this moment is that for the Problem/Solution posts, the five minutes of reading/twenty five minutes of tinkering is the right way for you guys to level up.

This is me being attentive to the experiential process favored by adult learners - watch one, do one, teach one. You get to watch me, do it for yourself immediately thereafter, and for a portion of these things you’ll have a coworker or friend asking for a screen share so they can see what you did.

This is a really nice chart that shows how and when to use simple Chat, as opposed to Cowork or Projects.

A broad hint here - almost everything I do involves using Cowork and giving it access to a local folder on my computer, sometimes a Github repository. I use Antigravity as my IDE (Integrated Development Environment) but I vastly prefer Cowork if there’s any browser access required.

Unlike the text only Claude Code running under Antigravity, with Cowork you can sit and watch what it does with a browser. Its surfing ability isn’t great, but by being able to see it you can whisk it past things that would completely flummox the headless browser control Claude Code does using Playwright.

Here are the Connectors that are configured on my system today. These are tools that Claude Cowork can employ as needed.

You can see Claude in Chrome there at the bottom. Exa is a search utility, Parabeagle is my local document indexing tool. Google Drive is a dumb file storage thing I dislike, Notion is a much smoother alternative, and Slack is for work chat. I can have Cowork read a Slack channel to get the details on a task, write it up in its sandbox so I can review it, and then push it to Notion so the others can see it.

The Cloudflare Developer Platform, Github, and Xcode are exactly as they appear - software development tools.

If you’ve only just got your first Claude Pro, the ones you want are:

• Notion - your “brain”, a place to store structured information.

• Exa - a quality search tool.

• Slack - handy if you have a team, it beats all the other chat systems.

I’ve used Claude Desktop every day for eight months straight. Even so, I got some benefit out of reviewing that chart. There is a LOT to learn and things are constantly changing. The good news is that the tools are much easier to connect and use than they were last year.

You will take some lumps time wise the first couple weeks, but you should quickly progress to the point where you get some of your time back. Once you’ve done that you’re free to digest additional capabilities without overloading yourself.