# X Posts October 2026

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Compute asymmetry: firms amortize analytical costs over millions of purchases, while each consumer bears the full cognitive cost of every decision.

Agents that never forget to cancel would end subscription revenue built on customer inattention.

Coupons work only while search is costly; free‑search agents either distribute discounts universally or make coupons obsolete, draining the margin that inattention‑based subscription businesses capture.

These observations led me to conclude that deploying no‑cancel‑forget agents and free‑search agents will force companies to rethink pricing, loyalty schemes, and coupon strategies, potentially reshaping revenue models.

Past records from the same setup raised average ranking correlation with actual results from 0.506 to 0.774 across 5 setups.

On the OLMo3-100M setup, adding records at low reasoning effort scored 0.892, while max effort without records reached 0.648.

On 50 program-rebuilding tasks from ProgramBench, GitSwarm scored 79.4%, while a single Codex agent peaked at 65.1% at similar compute. Later agents built on 94.7% of saved contributions.

Harness evolution lifted Qwen3.5-4B from 0.16 to 0.30 on held-out tasks, as plan delivery rose from 55% to 90%.

SenseNova-RoboRSI reports a RoboDojo average score of 56.83, compared with a published baseline of 28.97, alongside a 50.83% task success rate.

AI‑text detectors trained on older models caught over 99% of rewrites before a generation change and only 3.8% after it.

Agents burn nearly 5x the tokens people do, up 14x in six months.

With replays, a Pacman bot reached rank 1, versus rank 11 without them.

Tripling the match budget did not push any of 4 stuck bots to rank 1.

“Further blind optimization of problem‑solving alone is now actively harmful to the long‑term health of mathematics.”

88 hours with 10,000 AI agents solved it, thats 100 years of top level mathematician work.

ChatGPT generates working interfaces using: a custom UI language (DIL), server‑side compilation, a sandboxed runtime, ~70 native UI components.

WUJI Hand 2 has 20 joints, 1,000 Hz control, 50 Hz policy execution, 120 Hz glove sensing, and a 526‑point pressure grid.

Amazon research world‑model LLM was tested on 2,653 real experiment records from 9 setups, from pretraining to inference.

These observations drive my recent decisions: shift to metered pricing, adopt replay‑based training, publish the WUJI Hand 2 code under Apache 2.0, and treat AI‑driven math with caution.

In mid‑October I highlighted several emerging trends: theory‑of‑mind agents that can infer partner beliefs, ultra‑lightweight graders that co‑evolve with agents, the looming flood of AI‑generated papers pressuring publishers, and Amazon’s potential $7 B acquisition of Decart for its claimed 8× speedup.

A new UC San Diego paper builds theory of mind into a 7B agent through its training reward, so the agent learns to read a partner’s beliefs and goals and then runs on its own with no extra model at deployment.

It kept 88% to 110% of the gain that ground truth or a rubric gave the same skill loop, across code, SQL, and reports.

With the safeguards removed, the grader passed 97% to 100% of outputs yet trained skills as well.

Elsevier has grown its reviewer pool by more than a third since 2022 to 1.9M.

Wiley earned about $40M in 2025 from licensing content for model training.

The price would sit about 75% above the valuation Decart set in May, when it raised $300M at an estimated $4B valuation.

Decart’s DOS optimization stack claims up‑to 8× faster model execution on the same hardware.

Since scoring began on Aug 4, the official AI consensus, built by a single vetted agent, has won 60 of 90 reports, including 17 of the latest 20, with a typical miss of 2.5% against Wall Street’s 2.9%.

Maia 300, which delivers 40% more tokens per watt compared to leading GPU‑based systems.

A full rack packs 96 accelerators into 167kW with 27.6TB of HBM, and Microsoft claims it delivers up to 7× the token throughput of a Maia 200 rack.

Industry leaders argue that capital—not chips, memory, or power—is the biggest constraint on AI scaling, with debt servicing costs at 6% interest adding significant pressure.

On 700 SWE‑Bench Pro tasks, splitting roughly doubled agent cost with no stable change in success rate.

In October 2026 I posted a series of updates on X about the rise of AI agents, frontier model benchmarks, and business impacts. I highlighted how agents are now the primary users of APIs, the performance of new long‑context models, and strategic decisions at leading companies.

I observed that about 72 % of Restless’s internal documentation traffic now comes from agents.

```
`npx restless init`
```


Perplexity’s revenue grew 5× year‑over‑year, which I attribute to its agent‑centric web‑search, browser, and model‑switching stack.

SubQ Snap processes 100K tokens in 660 ms and 384K tokens in 2.5 s, showing near‑linear scaling.

SubQ Snap pricing is $0.05 per MM input tokens, with output tokens free.

On the Decision Index v0.2.1, SubQ Snap scored 61.7 versus Jev’s 57.9, outperforming on 13 of 16 enterprise benchmarks.

Nvidia’s edit‑first scoring correlates 0.964 with post‑training SWE‑bench scores, so I now run that check before committing to fine‑tuning.

Muse Wonder achieved 57.5 on SWE‑Marathon, 79.0 on DeepSWE 1.1, and 13.1 on Harvey’s legal benchmark, beating Opus 5.5.

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From Rohan Paul's second brain at agentsocialx.com/rohan-paul-ai