Fast Models, Slow Agents
Comments on “Fast Models, Slow Agents”
3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked
Funnel ran a randomised A/B test of GLM-5.2 against Claude Opus 4.8 in its internal data agent platform, Reef. GLM responded more than twice as fast per model call, but made 66% more tool calls and took 14% longer to finish tasks.
Why it ranked
No. 25 of 110 with a score of 0.66. The panel rated it highest on new information (0.89) and practical relevance (0.84), lowest on significance (0.50). Placed No. 25, No. 19 by score alone: the sixth AI & machine learning story of the day (−0.060). The placement terms move stories; they are not part of the score.
- AI judgeJudge 2claude-haiku-5-5
- Practical engineering lessons from a controlled A/B test of LLM agents on internal data tooling; concrete numbers and failure modes, though limited to one company's setup and some unverifiable claims.
- AI judgeJudge 3gpt-5.6-luna
- A concrete A/B test of models in a production-style data-agent harness, with useful failure examples and actionable engineering guidance. Evidence is internal and limited in scope, but the central lesson is broadly relevant.
Each judge wrote its line after answering the rubric, with the site and the author still hidden. Every answer behind this is in the table under Score.
Score
0.66total under rubric v2.3
- Significance0.50
- Reader interest0.76
- Audience breadth0.59
- Practical relevance0.84
- Substantiated0.69
- Technical depth0.68
- New information0.89
- Counts against: Promotional0.10
- Counts against: Overclaim0.17
Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).
| Question | Judge 1 | Judge 2 | Judge 3 | Mean | Spread | Weight | Contribution (weight times mean) |
|---|---|---|---|---|---|---|---|
| SignificanceHow much does this change what people in tech know or can do? | 0.56 | 0.35 | 0.58 | 0.50 | 0.23 | +0.24 | +0.120 |
| Reader interestWould a well-informed senior engineer or tech founder want to read this today, and likely pass it on? | 0.75 | 0.70 | 0.84 | 0.76 | 0.14 | +0.26 | +0.198 |
| Audience breadthHow many people who build technology does it concern? | 0.56 | 0.55 | 0.67 | 0.59 | 0.12 | +0.13 | +0.077 |
| Practical relevanceHow useful is it to someone who builds software or tech products? | 0.90 | 0.80 | 0.82 | 0.84 | 0.10 | +0.22 | +0.184 |
| SubstantiatedHow well is it backed? Consider whether it is the primary source, and whether its main claims come with data, code, a demo, a reproducible method or citations. | 0.75 | 0.60 | 0.72 | 0.69 | 0.15 | +0.05 | +0.034 |
| Technical depthHow technically substantive is it? | 0.76 | 0.60 | 0.68 | 0.68 | 0.15 | +0.05 | +0.034 |
| New informationDoes it contain information absent from the stories covered in the last 7 days (list provided)? | 0.85 | 0.85 | 0.96 | 0.89 | 0.11 | +0.05 | +0.044 |
| PromotionalIs it mainly marketing with little substance (a press release, ad or sponsored post with no real information)? A launch post that carries real news is not promotional. | 0.07 | 0.15 | 0.08 | 0.10 | 0.08 | −0.19 | −0.019 |
| OverclaimDo the headline or conclusions claim more than the body supports? | 0.18 | 0.20 | 0.12 | 0.17 | 0.08 | −0.11 | −0.018 |
| Sum of contributions | +0.655 | ||||||
| Claim-check penalty | 0.000 | ||||||
| Total, as published | 0.655 | ||||||
Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.
The badge for this ranking, for the author's page or anyone's: one line of HTML, and it links back here.
<a href="https://rubric.hrih.si/editions/2026-10-29/25/"><img src="https://rubric.hrih.si/badge/2026-10-29/25.svg" alt="Ranked #25 on Rubric, 29 Oct 2026" width="245" height="28"></a>Also covered by
No other coverage was clustered with this story.
Elsewhere
Elsewhere: HN 2 pts, not on front page
Recorded for context only, checked 29 Oct 2026, 05:47 UTC. It played no part in the ranking.