A **Block Engineering** benchmark post from **August 6, 2026**, signed by **Atish Patel**, about **Buzz** — the human + agent workspace launched on July 21 — asking a cost question: which agent team is **the cheapest one that reliably succeeds**? Three findings. **(A) A negative result, published in full**: on **Terminal-Bench 2.1**, **twelve team compositions** (pairs, triads, cheap swarms under a *frontier* model) were pitted against the solo agent each was built around, and **none beat it at equal cost**. The explanation is structural — a task that finishes in minutes *"doesn't have enough structure to divide"*, and *"More agents mostly buys you the cost of explaining it twice"*. **(B) The horizon reverses the result**: on **Long-Horizon Terminal-Bench** (44 tasks, one task worth hours of work, same lead **GPT-5.6 Sol** at *high* effort), solo finishes 15 tasks for 59.1%, +2 QuickBees 19 for 64.1%, +1 QuickBee +1 WorkerBee 19 for 69.5%, **+2 WorkerBees 20 for 71.5%** — a **+12.4-point** gain, of which 11.4 comes from tasks carried to completion. *"Same seats, opposite result, because the work is a different shape."* These runs ran at **3× the timeout**, solo included. **(C) Beyond a threshold, price stops buying quality**: solo on Terminal-Bench 2.1, **Opus 5 at *xhigh* effort is the most expensive run ($140.63) for 75.0%**, trailing six runs ranging from $20.08 to $109.82 and 79.5% to 88.4% — the stated cause is over-reasoning that drove 17 of 88 tasks to timeout. Among the six best runs, **a 5.5× price gap for an 8.9-point score gap**: *"choosing between them is not a quality decision at all. It is a budget decision."* The post proposes a taxonomy it owns as *ad hoc* — **QuickBee**, **WorkerBee**, **SmartBee**, plus the human as *"honorary bee"* — and two team forms, the permanent **Hive** that remembers your preferences and the disposable **Swarm** that remembers the project. Conditions: everything runs on **Harbor**, against real Buzz agents on a **live** relay, **one attempt per task, no retry**, prices fixed as of **2026-07-30**.
#Buzz#Block#agent teams
- **Atish Patel** — *« Building AI solutions @ Block »* · auteur unique du billet · publié le **6 août 2026** sur `engineering.block.xyz`.
**Recurring report from Mozilla**, *The state of open source AI*, **v1.0.1, July 2026**, introduced by a letter from **Raffi Krikorian** (CTO): seven sections, an interactive site, and a downloadable report. Thesis stated in the title of Section 1: *« The model layer has commoditized. Value accrues to the harness above it. »* **Capability state**: on the *Artificial Analysis Intelligence Index v4.1*, the best closed model scores **61** (Claude Opus 5) and the best open model **57** (**Kimi K3**), fourth overall and ahead of three of the largest closed labs; on the *Epoch Capabilities Index*, the gap is **6 points** (K3 at 156 versus GPT-5.6 Sol at 162), described as *« about one release cycle »*, with overlapping confidence intervals. **Sawtooth frontier**: open leads in frontend code (K3 at 1,679 Elo on LMArena Frontend Code Arena, six domains out of seven), contests agentic terminal work (88.3 versus 88.8 on Terminal-Bench 2.1), and cedes ground on professional knowledge work (Fable 5 leads K3 by 92 Elo on GDPval-AA v2). **Usage shift**: the share of OpenRouter tokens routed to open-weight models rose from a negligible level to a third by late 2025, then to a **majority by mid-2026**, with the seven highest-volume models all open-weight — the report itself noting that *« by request count, closed US providers still lead »*, the open lead being a token-volume lead concentrated in coding and agentic workloads. **The central contrast**: *« Open ships easy. Open deploys hard. »* — 79% of developers adding AI use open models versus 71% for closed, but only **53%** of open-model teams reach production **versus 63%**, and the gap widens with organization size (closed 54% → 73%, open 53% → 57%), which *« rules out a resources explanation »*. The stack maturity map (48 components, 9 layers) shows two consistently cold columns — **standardization** and ***enterprise readiness*** — identified as the operational gap. **Section 5**: *« The agentic harness is another user agent »*, and *« The model is eating the harness »* — on every model where both exist, the lab's own harness now wins, the 21.8-point gap having compressed to about 3. Hence the formula: *« A harness tuned tightly to one lab's weights… degrades on anyone else's model, so the tighter the tuning, the less swappable the weights underneath. Lock-in arrives as a side effect of optimization. »*
#Mozilla#state of open source AI#open weights
**Mozilla** — éditeur du rapport · avec une introduction signée **Raffi Krikorian** · *Chief Technology Officer*. Publié en **juillet 2026** (v1.0.1). Données issues de sources tierces créditées (Artificial Analysis, Epoch AI, OpenRouter, LMArena) et d'une enquête propre menée avec **SlashData** (*Mozilla / SlashData 2026 developer survey*, n = 1 410 sur la question des freins).