Vol. I · No. 1Edition: Mid-morningEst. 2026AboutSearchRSS

The Sand Paper

Finding grains of sand with grains of sand.
A Journal of Intelligence Transfer
ArchiveOctober 2026
Lead Story · Models & Hardware

TensorFold Recipes Arrive for GLM 5.3 Flash and Qwen3.8 27B

Mia's AI Lab ships two TensorFold recipes: 2.7M KV tokens on two Sparks, 220 tok/s on one.

TensorFold, Ash Hart’s engine, headlined October 1 on the Spark timeline. @MiaAI_lab published a recipe for GLM 5.3 Flash EXL3 on two DGX Sparks. Defaults: 1M context, a 2.7M-token KV cache pool and four concurrent streams. Prose decode runs 60 tok/s single stream and 108 tok/s at four streams, with prefill near 1,950 tok/s. She thanks @ashxhart for the engine and calls it the best model to run on two Sparks.

The same lab followed with Qwen3.8 27B on one Spark: 256k context by default, extendable to 1M with YaRn, a 2.5M-token KV cache and eight concurrent sessions. Prose decode runs 58.3 tok/s single stream, 108.5 at two streams and 220 at eight, which @MiaAI_lab puts at 2x faster single stream with TensorFold. Full vision support, image and video, ships with the GLM recipe.

@ashxhart, the engine’s author, reports 63 tok/s decode on GLM 5.3 across two Sparks, and @markargyelan_ measured the pair drawing 37 to 45 watts. Asked whether decode could reach 100 tok/s, he answers “I do think it’s possible.” @datachad calls the 1,950 tok/s prefill the line that matters, against the 13 minutes a cold 262k fill takes on his M3 Pro.

From posts by @MiaAI_lab, @MiaAI_lab, @ashxhart

Agents & Coding

Ask Claude to Draw Your App's Architecture as a Zoomable Canvas

Feature work outran his architecture notes, so he asked Opus 5.5 to map the app.

@robinebers asked Claude Opus 5.5 at high effort to build an interactive canvas of his app’s architecture, zoomable and clickable, after two months of shipping features faster than he could document them. The result drew connected graphs of what each part does, with step-by-step walkthroughs of flows like signup, payment and file upload through R2 storage and Modal workers. Replying to a flowchart skeptic, he notes the canvas lights up every major action on click, with an explainer for each step.

From posts by @robinebers, @robinebers

Models & Hardware

Subscriptions Beat API Pricing, With the Multipliers to Prove It

Claude Max 20x really is 20 allowances; OpenAI's Pro 200 was downgraded to 10x from 20x.

@PawelHuryn burned slices of popular plans and priced the calls at public API rates to put numbers on subscription value. OpenAI’s Pro 100 at $100 a month buys 5x the Plus allowance and Pro 200 at $200 buys 10x, downgraded from 20x. Claude’s Max 20x at $200 delivers 20x the Pro allowance, not 10x, and Meta’s Muse Code High Usage at $15 is 5x, not 3x. He ranks SuperGrok the cheapest western subscription for agentic work.

From @PawelHuryn

The Memory Bandwidth Ceiling for Local Decoding, in Two Lines

M5 Ultra tops out near 225 tok/s, a DGX Spark near 39; speculation pushes past both.

@plotarmordev reduces local decode ceilings to one division: memory bandwidth over data read per word. An M5 Ultra at 1.2 TB/s moving 5.3 GB per word at 5-bit caps near 225 tok/s. A DGX Spark at 273 GB/s moving 7.0 GB at NVFP4 caps near 39 tok/s. Speculative decoding lifts the Mac from about 114 to about 178 tok/s, and he notes one Spark recipe reaching 49 tok/s, above its ceiling.

From @plotarmordev

Svpino Reports a Fine-Tuned Qwen3 4B Beating Claude Sonnet 4.6

The AWS fine-tune beats the out-of-the-box model and Sonnet 4.6, with the workshop code public.

@svpino reports that fine-tuning Qwen3 4B on AWS produced a model beating both the out-of-the-box model and Claude Sonnet 4.6. He argues a tuned small model can be cheaper and faster to run, and links a workshop codebase covering supervised fine-tuning, direct preference optimization and reinforcement learning from verifiable rewards. @cocoloba_ reports the same effect in vision: a small model trained on squash clips finds shots at 0.92 F1. The best frontier route he tested got 0.87, at roughly 1,000x the cost.

From @svpino

GPT-6 Astra Tops the Creative Writing Board; Opus 5.5 Sits 7th

EQ-Bench's leaderboard has ChatGPT's model first and Claude's Opus 5.5 seventh; seven prompts follow.

@alex_prompter notes everyone is writing with Claude Opus 5.5 this week, but GPT-6 Astra holds #1 on EQ-Bench’s creative writing leaderboard while Opus 5.5 sits 7th. His seven prompts for human-sounding output include a voice match over five past posts, an opinion-first format under 150 words, and an interview that asks three questions before writing. Others strip robotic lines, hunt for sentences anyone could have written, cut the draft to half its length while keeping every fact, and run a reply test from his harshest follower.

From @alex_prompter

AI in Accounting & Tax

A One-Person Business Runs on Claude, With Approval on Every Send

The guide cites 59% of small businesses carrying invoices over 30 days late, averaging $17,700.

@AISystems_hq lays out 12 Claude processes for a solo business, from chasing invoices to closing the month. One rule covers all of them: Claude drafts, sorts, flags and files, and never sends, posts or pays without approval. The guide cites Xero research that owners spend about 36% of the week on admin, 15 to 20 hours. It also cites 59% of small businesses carrying invoices over 30 days late, averaging $17,700, with each process running as a skill on a schedule.

From @AISystems_hq

Industry

Anthropic's Courtship of Religious Leaders Draws Fire

An NYT report says Anthropic hosted faith leaders under NDA and discussed Claude's moral standing.

@DavidDecosimo flags an NYT report on Anthropic's courtship of religious leaders. It describes the company inviting religious leaders to San Francisco, having them sign NDAs, and making the case that Claude has a soul and moral standing. He calls the effort deeply troubling. In the replies, @brianchau57 cites earlier reporting that AI safety donors paid religious NGOs $3.3M for statements on AI.

From @DavidDecosimo

Sources

  1. @MiaAI_lab on X
  2. @MiaAI_lab on X
  3. @ashxhart on X
  4. @AISystems_hq on X
  5. @PawelHuryn on X
  6. @plotarmordev on X
  7. @svpino on X
  8. @robinebers on X
  9. @robinebers on X
  10. @alex_prompter on X
  11. @DavidDecosimo on X
Wednesday, September 30, 2026Back to the ArchiveFriday, October 2, 2026