Product announcement post from **Block Engineering** signed by **Thomas Petersen** (*Principal Designer & Builder*), published on **August 18, 2026**, ~1,800 words across thirteen short sections, introducing **Buzz Projects** — a **software forge hosted on its own relay**: Git repositories, branches, pull requests, issues, review and merge, multi-repo projects, an activity feed, all linked to conversation channels. The post's standfirst and thesis: *« Coding agents are the terminal for your computer. Buzz is the terminal for your network. »* Three contributions. **(A) A trust doctrine grounded in *ex post* proof rather than *ex ante* authorization**: on one side *« No forced guardrails, no limitations on what your agents are allowed to help you with »*, on the other *« Every push, review, approval, and merge is a signed Nostr event. If an agent authors a patch, you can see which agent produced it and which human authorized that agent to act »*; the section closes on a stated direction — *« we are already exploring ideas around agent trust protocols informed by past behavior »*. **(B) Git interoperability without proprietary tooling**: *« These are standard git repositories… You can fetch, clone, pull, and push over plain Smart HTTP, with no custom tooling or wrapper CLI required »*, with the clé Nostr serving as a single identity — *« The same npub that signs your messages signs your pushes. »* **(C) A distinction between execution surface and network presence**: *« A terminal gives an agent somewhere to execute commands and change files, but it does not give it a persistent place in the network. Buzz does. »* The post produces no figures and contains no outbound links; it qualifies itself as preliminary six times (*« still very basic »*, *« fairly elementary »*, *« still under experiments »*), and Projects lives under the **Experiments** tab of Buzz Desktop.
#Buzz#Buzz Projects#Block
**Thomas Petersen** — *« Principal Designer & Builder »* chez **Block** · auteur unique et signataire du billet ; première apparition dans le corpus. Publié le **18 août 2026** sur le blog **Block Engineering**. Troisième signature Block sur Buzz en un mois · après Tyler Longwell (21 juillet) et Atish Patel (6 août) · et la première non-ingénieur.
Announcement from **Meta AI Research** published on **August 5, 2026** (stated reading time: 4 minutes, no individual byline): **Muse Code** in beta, *« a terminal coding agent »*, and the model that powers it, **Muse Spark 1.2**. Meta itself frames the launch: *« This marks our next step toward the frontier, with larger and much more capable models on the way. »* **Three architectural elements on the harness side.** **Asynchronous background agents** that *« remain active throughout each session, rather than being spawned for individual tasks »*, avoiding redundant information gathering and reducing the need for steering. A **local event log** where *« every model call, tool run, approval, and edit is appended »*, making the runtime a system that is *« replay-exact and restart-safe »*, able to resume exactly where it left off after a crash. And **three skills shipped out of the box**: `/plan` (turns a task into a plan submitted for approval), **`/grill`** (stress-tests the plan *« until it holds up »*), and `/goal`. **On the model side**, Meta claims **model-harness co-training** (*« to maximize harness compatibility »*, with harness trajectories sampled via rejection sampling and recipe optimizations for goals, compaction, and sub-agents), **long-horizon** training (whole-repo generation, end-to-end projects, self-research, with planning, goal conditioning, and context compaction), and a **self-improvement loop** where Muse Spark 1.1 generates the environments and instruction templates and then grades candidate solutions, producing a training set for the 1.2. **What the published charts show**, without the text commenting on it: the four comparisons — Terminal-Bench 2.1, DeepSWE 1.1, an internal Meta benchmark, and the GPU kernel optimization case study — place **Muse Spark 1.2 behind Opus 5 in all four cases**, including on Meta's own proprietary benchmark (70.6% versus 79.4%) and on the case study, where the model finishes fourth out of six (+68.7% versus +74.0%). **A reading caution on the version gain**: on the two public benchmarks, 1.1 is measured with `mini-swe-agent` and 1.2 with Muse Code, so the 6.7-point gap conflates model and harness. On the internal benchmark, the only comparison where no harness is mentioned, the 1.1 → 1.2 gap drops to **2.3 points**.
#Meta AI Research#Muse Code#Muse Spark 1.2
**Meta AI Research** — publication institutionnelle sans auteur nommé · sur `research.meta.ai`. Le billet renvoie à un **rapport** pour la méthodologie d'évaluation · non repris ici.