Applications

TL;DR Between late June and late September I moved my daily setup through Sonnet 5, Opus 5, Fable 5.1, Opus 5.5 and Sonnet 5.5. Haiku stayed at 4.5 the whole time. I never saw a Haiku 5 in any log. There is no controlled benchmark in anything I kept. What follows is operational evidence: what I changed, what broke, what I verified, and what I would have gotten wrong if I had trusted what a model told me about itself. The changes that mattered were small: a per-model effort block in settings.json and a subagent default model. The expensive lessons were two traps. One is a bug where background-job subagents silently ran Haiku 4.5. The other is that a worker’s report of its own model cannot be believed. If you want the earlier, more general version of this, see re-tuning my setup for a new Opus model. This is what the next five releases taught me. The ladder These are first-seen dates from my own records: commit trailers in my repositories and, where I still have them, session transcripts. They are when I started using each model, not necessarily when it became available. ...

TL;DR Jev is TypeSafe AI’s first “System One” model, public since 2026-09-15. You send it a state object and a few typed questions, and it returns probabilities in 70 to 500 ms. I wrote a design doc for using it in my own tooling, and then rewrote it twice in two days because the first draft was wrong in ways I only noticed by rereading the sources. Nothing is built. There is no client, no table, no spike, no measured number of mine in this post. This is a review of a third-party product before I adopt it. Every accuracy and cost figure below belongs to the vendor or to a third party, and I label it that way. ...

TL;DR I added an “Enhance” button to my photo viewer, backed by RealESRGAN running in ComfyUI on the Mac Studio. Its stated job was to help me read model plates and serial numbers in blurry surplus photos. I benched it against a plate where I knew the right answer. At small glyph sizes it did the opposite of its job: crisp, confident, wrong text. G1312B-60050 came back as 01D12B-(0)00. It is not a bad model pick. Four different upscalers garbled the same lines the same way. The usual advice (pre-upscale with a classical filter first) changed nothing. The failure is the generative prior, not a starved input. What I do now: the original pixels stay the source of truth, there is a text-safe sharpen view that invents nothing, and the AI view composites real pixels back over anything that looks like fine detail. A wrong model number off an enhanced photo is a wrong comp, which is a wrong bid. This one was worth catching before it spent money. Why I wanted it The most valuable pixels in a surplus lot photo are the smallest ones. The difference between a generic median price and an exact-model price is often a part number on a data plate, shot from a few feet away at a resolution where a 13-point label is about twelve pixels tall. I already pull the full gallery for every lot and archive it, so I had the best pixels the seller provided. They just were not enough for some plates. ...

TL;DR The screens on eight surplus all-in-ones show an ops board: bids, ship queue, pickups, repair backlog, cluster health, weather and traffic. They sit on desks around the office and do double duty: a workstation when someone sits down, the board the rest of the time. The same machines are on their way to becoming k3s worker nodes (one has joined so far), and that build gets its own post. It’s one static HTML page in Chrome kiosk mode. No framework server, no Kubernetes workload. Ansible deploys it. 15 scenes sit in a weighted registry and rotate off the wall clock, about 34 seconds each, so eight screens agree without talking to each other. When a fetch fails the page keeps the last good data and shows its age. A board with stale data and a warning beats a blank screen. It’s a different animal from my older home signage system, which is a k8s app with Raspberry Pi kiosks. One real thing it did: on 2026-09-06 the home cluster’s daily Longhorn backup had stopped completing, and the board’s home-cluster card showed the critical alert. Not the same project as the home signage I’ve written about digital signage on k3s before: an Angular app, a handful of Flask services, MQTT, Raspberry Pis driving displays. That’s a proper application and it lives in the home cluster. ...

TL;DR An unmodelled repair backlog becomes a pile of dusty things you feel bad about. Ask me how I know. Four ticket states: open, blocked, done, abandoned. blocked and abandoned are the two that do the work. abandoned is a feature. A queue you can’t quit is a queue that grows forever, and “I’ll get to it” is not a state. Parts are modelled as purchases and uses separately, because you buy a bag of ten capacitors and use two. Otherwise your cost-per-repair is nonsense. Labour gets logged per worker with an hourly rate, and that number flows all the way back into what I’m willing to bid on a lot. The no-go rule does more for profitability than any repair skill I have: if parts and time exceed roughly 60% of realistic resale, or resale is under about $120, it doesn’t get a ticket. Other people work this queue. Four access roles keep them on inventory and tickets and away from anything financial. Why a pile forms Buying surplus means buying broken things, on purpose, because broken is cheap and some broken things are trivially fixable. A cassette deck with a perished belt is a $4 part and twenty minutes. A workstation that won’t POST is often one reseated stick of RAM. ...

TL;DR The work between “I own this thing” and “money arrived” is mostly paperwork and printing, and it’s where the hours actually go. Listings get built through the marketplace API, not by driving a browser — structured item specifics as JSON beat fighting a typeahead widget every time. Drafts are created automatically. Publishing is always manual. Publish is a money-moving, buyer-facing, irreversible action; it gets a human every time. Barcode labels render from the inventory record itself: ZPL → PDF → print job to a thermal printer, triggered from the item’s page. No separate label app, no retyping serials. Pack slips print on a schedule, and the job verifies the printer’s page counter moved before marking the slip printed. Optimistic “it printed” flags are how orders ship without paperwork. Anything bulk or financial — repricing, printing five-plus labels — sits behind an explicit confirmation. Cheap friction in exactly the right places. The boring half is the expensive half Sourcing gets all the attention. I’ve written about the funnel that finds lots and the data model that tracks the units. Both were interesting problems. ...

TL;DR You buy a lot. You sell units. That asymmetry is the whole data-modelling problem, and spreadsheets lose to it immediately. The spine is four tables: purchase_lot → inventory_item → listing → sale, with join tables where the relationships are genuinely many-to-many. Cost gets allocated from the lot down to units, and the allocation locks once the first unit sells. Retroactively re-allocating cost after a sale is how you accidentally rewrite last quarter’s profit. A unit walks a status machine: sourced → processing → drafted → listed → sold → shipped → reconciled, with dead and personal as the two honest escape hatches. Selling quantity-N on one listing is N sales, not one sale with a quantity column. I modelled it the wrong way first and spent August unpicking it. Packing several units into one sellable bundle, and splitting a bundle back into units, both need to conserve cost exactly. Getting that wrong is silent and it shows up as fake margin. The mismatch Here’s the transaction that starts everything: I pay $340 for a pallet described as Assorted Networking and Computer Equipment. One line item, one payment, one date. ...

TL;DR Active eBay listings are asking prices, not realized prices—they lie. Use SOLD comps (~90-day history) to anchor your math. Card identity is everything: name + set + edition + variant + grade. “Charizard” is not a price; “Base Set Unlimited Charizard, PSA 9” is. Sell-through velocity (sales per week) predicts how long your capital sits locked up and is the variable most buyers ignore. Expected value = (median sold price × (1 − fee rate)) − shipping − grading. Apply a 30% margin of safety before you bid. Confidence gates protect you: skip lots where you can’t find ≥3 grade-matched comps or where the price spread exceeds ~6× the median. Buying Cards Rewards Discipline I’ve overpaid for Pokémon lots I “knew” were steals. The problem was never the cards—it was that I was bidding on nostalgia and gut feeling, not math. ...

TL;DR Three production MCP servers I run: a docs/wiki wrapper, a personal-finance aggregator, and an inventory-ops tool for a side project. Type hints are your schema. The FastMCP SDK extracts JSON Schema from Python docstrings and type annotations — you almost never hand-write schemas. Flatten nested APIs. If the upstream API returns nested JSON, unwrap it in the MCP tool and return clean markdown or a simple dict — agents prefer predictable structures. Reads always-on, writes gated. Use environment variables (MCP_WRITE=1) or similar to gate mutation tools; put mutations behind a validated service layer and audit every call. Log to stderr, not stdout. Stdio protocol uses stdout for JSON-RPC; anything else breaks the connection. The lineup I’ve written three MCP servers from scratch over the last few months. This is the cookbook — concrete patterns, not theory. If you haven’t read the intro to writing MCP servers, start there; I won’t re-explain the anatomy. ...

TL;DR Cover art was the gateway drug. The same local ComfyUI install that generates this blog’s headers also strips the cluttered background off a photo of hardware on my bench, upscales a small generation to retina resolution, and batch-produces a consistent set of illustrations from a prompt template. Two Mac Studios mean I can fire a batch at one box and keep working on the other. It’s all driven from scripts and agents, and it all costs $0 per image because it never leaves the house. ...