The future is arriving badly explained

A celiac antibody shows less damage in a small gluten test
First Tracks reports less intestinal damage after a two-week gluten challenge in a randomized antibody trial, with 32 adults evaluable. The early human finding does not show that people with celiac disease can abandon a gluten-free diet. These company-reported, exploratory results come from a study primarily testing safety.
First Tracks / GlobeNewswire · 12 October 2026
A porous chip writes a tiny message into DNA
Researchers used a porous silica coating on microelectrode chips to write four DNA strands and recover a 60-byte message with error correction. The film increases the surface available for synthesis. This is a small laboratory demonstration, not proof of cheaper storage or a commercially scalable writing system.
Microsystems & Nanoengineering · 12 October 2026
Silicon nanowires without a high-temperature furnace
A fabrication process produced suspended silicon wires as narrow as 50 nanometers while keeping its maximum temperature at 180°C. That could make these structures easier to add to existing electronics without high-temperature processing. The study demonstrates working devices, not manufacturing yield across full wafers or a qualified industrial process.
Microsystems & Nanoengineering · 12 October 2026
A small converter boosts 25 volts to 400
A 200-watt laboratory converter raised 25 volts to 400 at about 96% efficiency under full load. Its reported peak efficiency, 96.64%, occurred at a lighter 80-watt load. The result addresses a power-conversion problem relevant to low-voltage energy sources; field reliability, commercial cost and larger-scale performance remain unproven.
Scientific Reports · 12 October 2026

Three original studies and one company trial report published on 12 October 2026. Laboratory and early clinical findings are not proof of routine use.

A World in Perspective

In Charts.

All Charts

In Charts12 Oct 2026

Battery Incidents Barely Fell. Capacity Almost Doubled.

Almost the same incident count. Much more capacity.2024 VS 2025 · EPRI OCTOBER 2026 REPORT

These are report-snapshot values, not a complete count of every battery failure. Annual events divided by installed GWh are not an individual site's failure probability. The public CSV has 23 events for 2025, versus 24 in the report; the versions are not combined.

Data: EPRI · October 2026 report, pp. 6–7 · Calculation and chart: VastkindMeasured: 2024–2025 · Report: October 2026 · Retrieved: 10 Oct 2026

The denominator does most of the moving

For a community considering a large battery installation, a falling incident rate sounds reassuring. But the numerator matters too: did incidents become rare, or did the amount of installed storage grow much faster?

In EPRI's October 2026 report, the recorded annual count falls from 26 to 24, a decline of 7.7%. The published capacity figure rises from 343 to 634 gigawatt-hours, up 84.8%. Divide one by the other and the result falls from 7.58 to 3.79 incidents per 100 GWh, a 50.1% decline. The large change in that ratio is not a halving of the incident count.

A useful ratio is not a site-risk forecast

The report tracks major internal thermal incidents in stationary lithium-ion storage, including utility and commercial or industrial installations. It is not a census of every malfunction, electric-car battery or home battery. Reporting is incomplete, and each incident counts once regardless of its severity.

The denominator is stored-energy capacity, not electricity generated, power in gigawatts or operating years. Newly installed capacity can enter the annual stock without having operated for a full year. We also could not independently verify that the proprietary capacity series covers every installation type in the incident count.

The fair reading is narrower than “batteries are half as dangerous”: in this report, recorded incidents fell slightly while the installed capacity denominator rose sharply between these two years. A decision about one installation still depends on its design, operation and consequences of a failure.

Sources, Method & Data

Snapshot and calculation. Four aggregate values come from printed pages 6–7 of EPRI report 3002034920, dated October 2026. Its PDF was created on October 8; the exact first-publication day was not established. This chart is a new explanation of 2024–2025 data, not a new incident count for October 2026. Each ratio is annual recorded events / cumulative installed capacity in GWh × 100. Changes compare 2025 with 2024. Input capacities are already rounded; two decimal places on the ratio do not imply extra measurement precision.

Source versions do not fully reconcile. The public stationary-storage CSV retrieved on October 10 carried a September 29, 2026 Last-Modified header. It contains 26 events dated 2024 and 23 dated 2025, versus 26 and 24 in the newer report. Some regional and confidential-case totals also differ. A version difference is plausible, but no case-by-case revision history resolves it. We reproduce the report aggregates, not a reconstructed current global total; no cases have been invented or added to the CSV.

Coverage and denominator. EPRI includes major thermal events caused by internal battery-system failures and excludes external causes such as wildfires. Some cases are confidential. The report's source figure cites Benchmark Mineral Intelligence's BESS Forecast Q1 2026, China Energy Storage Alliance and Korea Electrical Safety Corporation. The underlying capacity records were not independently accessible. Matching of utility, commercial/industrial and UPS coverage between numerator and denominator remains unverified. This simple annual quotient is not exposure in GWh-years, an individual failure probability, a severity-adjusted measure or the report's separate age-based analysis.

EPRI October 2026 report snapshot; own division
Year	Recorded incidents	Capacity (GWh)	Incidents per 100 GWh
2024	26	343	7.58
2025	24	634	3.79

Graphic and rights. Own calculation and original graphic using four published aggregate facts; no EPRI figure or layout reproduced. EPRI's report is © 2026, all rights reserved, not an open-licensed dataset. The accessible CSV is linked for source-version checking, not substituted for the report.

EPRI's October report records 26 incidents in 2024 and 24 in 2025. A much larger capacity denominator roughly halves the ratio, not an individual battery's risk.

Explore the Chart

In Charts9 Oct 2026

Can AI Customer Service Solve the Same Case Four Times?

Repeat the same case. The result can change.97 SIMULATED CASES PER MODEL · FOUR RUNS EACH

Numbers inside the bars count cases, not individual runs. “Every time” means success in all four recorded runs, not four chances to succeed once. Two examples, not a model ranking.

Data: Sierra Research · τ-knowledge · Chart: VastkindRecorded: 3–4 Aug 2026 · Submission: 4 Aug 2026 · Retrieved: 7 Oct 2026

Getting it right once is the easier bar

Imagine asking customer service to replace a lost card. A successful interaction is encouraging. The harder question is whether the same request still works when the conversation takes a different path.

In these published simulations, Claude Opus 5 succeeded at least once on 64 of 97 cases; Qwen 3.8 Max did so on 71. Require success in all four runs, and those counts fall to 31 and 34. The middle segment shows cases that sometimes worked and sometimes did not.

A benchmark, not a bank

These are recorded evaluations in a fictional banking environment, with an AI playing the customer. Vastkind grouped the original scores; we did not run the agents again or independently regrade their conversations. The chart measures neither real customer satisfaction nor financial safety.

Both agents received the same embedded case definitions and tool access, but used different reasoning settings. These two examples do not establish a general model ranking or equal-cost comparison. Four runs per case are a small sample, not a guarantee about the next run.

A further reproducibility limit: 15 of the 97 embedded task definitions differ from the code revision named in the run files. They match between the two files analyzed here. The reason for the repository discrepancy is unresolved.

Sources, Method & Data

What we counted. The τ-knowledge banking_knowledge AllTools run files contain 97 case definitions and 388 runs per model, four distinct trial IDs per case. We treated a logged reward within 0.000001 of 1 as success, matching the project metric, then grouped cases by zero, one to three, or four successes. Both selected files contain no infrastructure-error terminations. Bar widths are group counts divided by 97; labels give the counts.

Settings and timing. Qwen 3.8 Max used xhigh reasoning; Claude Opus 5 used max. Both used GPT-5.2 with low reasoning as the customer simulator and a 200-step limit. The metadata evaluation date is August 3, 2026; raw timestamps span August 3–4 without a stated time zone. The submission date is August 4; the hosted files also carry August 4 modification timestamps. This is a new explanation of that snapshot, not a new October test or a comprehensive current leaderboard.

Denominators matter. Individual-run success was 189/388 for Claude and 214/388 for Qwen. That average is not the first attempt. “At least once” counts cases with one or more successes; “every time” counts cases with four. We make no independence assumption or statistical-significance claim.

Original records. Repository snapshot; MIT code license. Own aggregation and graphic, not a copied source figure. The hosted conversation files carry no separate license notice; full dialogs are not republished here.

Claude Opus 5: submission metadata; original run file (about 240 MB). SHA-256: 80bd60cd0dc19855e685b85ef2c40efcbc2dabae1ffa16988b40b781cbeee2a3.
Qwen 3.8 Max: submission metadata; original run file (about 240 MB). SHA-256: 8c8191c43dfb2d21c1322cc154740e5e6044151837ab817c2cbbcd13ffeb626e.

Task-version boundary. The run files name code commit fc0055dc4e0a316c3f83133267fbd6faaa770992. Fifteen embedded task definitions differ from that revision and from the repository snapshot above. Our counts describe the scores in the published run files. They do not establish an exact reproduction from the named code revision. Other model results were not included because their comparability was not fully verified.

Recorded outcomes: cases, not runs
Model	0 of 4	1 of 4	2 of 4	3 of 4	4 of 4	Total
Claude Opus 5	33	11	12	10	31	97
Qwen 3.8 Max	26	14	5	18	34	97

Related: Can AI spot an IKEA assembly mistake? A different capability test, with its own limits.

Explore the Chart

In Charts6 Oct 2026

Can AI Spot Your IKEA Mistake?

Can AI judge an IKEA assembly photo?BEST TESTED SCORE: 28% → 80%

Lime line: successive best scores among tested models. Gray dots: other tested models. 60 photos, three furniture builds; photos + manual + tools. A retrospective comparison, not a physical assembly test.

Data: Aiden Ament & Greg Burnham · Epoch AI · CC BY 4.0 · Chart: VastkindReport: 23 Sep 2026 · Model releases: Nov 2025–Sep 2026 · Accessed: 6 Oct 2026

A practical test of visual reasoning

A photo, an assembly manual and tools. Epoch’s test asks models to judge the assembly and, if something is wrong, identify the relevant steps and explain the mistake. The published benchmark score rose from 28.3% for Claude Opus 4.5 to 80% for GPT-6 Astra.

Not a promise to fix your furniture

The sample covers only 60 photos from three builds and is partly graded by another AI. It does not establish reliability on other furniture, real-time repairs or physical assembly. The score is not simply the proportion of flawed builds flagged, and the tested models are not a complete historical leaderboard.

Sources, Method & Data

The 21 source rows are ordered by model release date as reported by Epoch, not test date. The line retains each new record among those models; it is not a complete history of all available AI. GPT-5 and Gemini 3 are absent. Published scores are plotted unchanged ×100. The endpoints are 28.3333% and 80%, a gain of 51.6667 percentage points. Source standard errors are 5.8665 and 4.9289 percentage points; all model standard errors are in the table below. They are not 95% confidence intervals.

The dataset has 42 intentionally flawed and 18 correct photos from STÄLL, TONSTAD and GULLABERG builds. Models receive instructions and image/Python tools. Correct step identification and an adequate error description matter; a lenient LLM grades descriptions. The score is not simply the fraction of flawed builds flagged. Some exported scores imply half cases when multiplied by 60; the public method does not explain that granularity, so no exact “x out of 60” count is inferred. No human-performance comparison. Accessed October 6, 2026.

Benchmark methodology · Original chart dataset

All 21 tested models; original Epoch scores
Model	Release date (Epoch)	Score (%)	Standard error (pp)
Claude Opus 4.5	2025-11-24	28.3333	5.8665
GPT-5.2	2025-12-11	38.3333	6.3298
Claude Opus 4.6	2026-02-05	28.3333	5.8665
Gemini 3.1 Pro	2026-02-19	26.6667	5.6332
GPT-5.4	2026-03-05	37.5000	6.2465
Claude Opus 4.7	2026-04-16	33.3333	6.1372
Kimi K2.6	2026-04-20	21.6667	5.2301
GPT-5.5	2026-04-23	44.1667	6.4101
Claude Opus 4.8	2026-05-28	42.5000	6.3807
Claude Fable 5	2026-06-09	35.8333	6.1859
GPT-5.6 Sol	2026-07-09	56.6667	6.3409
GPT-5.6 Terra	2026-07-09	54.1667	6.4321
GPT-5.6 Luna	2026-07-09	42.5000	6.3807
Kimi K3	2026-07-16	34.1667	6.1170
Gemini 3.6 Flash	2026-07-21	23.3333	5.3766
Claude Opus 5	2026-07-24	60.8333	6.1859
Gemini 3.7 Flash	2026-08-13	26.6667	5.6332
Claude Fable 5.1	2026-09-01	70.0000	5.7244
Qwen3.8 Max	2026-09-01	20.0000	5.2076
Gemini 3.8 Flash	2026-09-02	31.6667	5.9383
GPT-6 Astra	2026-09-03	80.0000	4.9289