A Reddit user said Qwen3.8-Flash-Next performed well on coding and adjacent tasks, including evaluating open-source repositories and running models. In general research, however, the user reported confusion, verbose and dense prose, and difficulty maintaining consistent answers. The test used Pi with llama.cpp, UD_Q4_K_M quantization, and MTP, with sampling parameters copied from Qwen3.8 27B.
A Reddit user compared 10 coding agents using the same Qwen 3.8 27B Q4 model through llama.cpp on a MacBook Pro M5 Max with 64 GB of memory. Across five trap-containing tasks, every agent passed four or five tasks, while token consumption, completion time, cache reuse, and network behavior varied substantially; Pi used the fewest median tokens, while Qwen Code used the most. Codex CLI, Cline, OpenCode, and goose attempted an internet connection on every run, although the sandbox blocked those requests.
Researchers and Olam Labs ran multi-agent experiments in Minecraft and Mindustry using an open-source Minecraft harness built on Mineflayer with a Pi-based agent loop. In one three-hour Minecraft run, 50 GPT-6.1 Sol agents shared a sandbox, rationed food, established rules and roles, and exchanged about 5,900 messages. The authors say the research could help investigate emergent coordination and multi-agent dynamics in AI alignment.
SafeAgents is an Apache 2.0-licensed local proxy that checks coding-agent tool calls before the harness receives them. It combines two request-based checks with about 110 deterministic atif-scan detectors to identify undeclared tools, unexpected hosts, sandbox side channels, network bypasses, namespace escapes and reward-hacking attempts. An optional OpenAI-compatible judge can assess milder findings and may run locally. Flagged calls are replaced with a text notice, while --log-only mode records attempts without altering execution. The project supports OpenAI Chat Completions and Anthropic Messages, provides setup guides for 12 harnesses and reports end-to-end CI tests for six.
Zest creator Yudhanjaya introduced Zest as an open-source desktop interface that bundles the Lemonade model server (which installs llama.cpp and needed backends) and the Pi agent, giving people who do not want to deal with command-line servers and parameters a friendly way to run local models. The creator says users download an AppImage, go through a start tour, and get sane defaults baked in, such as a 128k context, plus model recommendations the creator says were tried and tested on systems with 10GB to 26GB VRAM; these claims have not been independently verified. The creator says Zest is not aimed at advanced users and points them to Strata and Opencode in the Help section. A separate product listing describes invoking an agent workflow with a keyboard shortcut and gesture.
A r/LocalLLaMA user compared UkisAI’s Swift 1.5 fine-tune of Qwen3.8-Flash-Next with Unsloth’s quantization across Halogen, gufo and llama.cpp on an Asus ROG Flow Z13 with 128 GB of memory and a 70 W power limit. Swift produced 56,000–63,000 output tokens across 11 Pi coding-agent tasks, versus 75,000–99,000 for Qwen3.8-Flash-Next, and the gufo Swift setup completed them in 24 minutes compared with 33 minutes for gufo with Unsloth’s UD-Q4_K_XL. The results were mixed on math and LiveCodeBench, and the author cautioned that they came from a single run, so differences of one or two solved problems could reflect chance.
A community post asks whether readers sandbox pi and, if so, what approach they use. It mentions Docker sbx, regular containers, DevContainer and pi's Gondolin sandbox extension, but the excerpt provides no implementation details or comparisons.
In a report on r/LocalLLaMA, a user said an approximately 800-line Splash 1.4.0 fork running Qwen3.8-Flash-Next at ISTA GSQ-RCO Q2_0 achieved 48.8 tokens per second on a 24GB M5 MacBook Air by streaming nonresident experts from SSD. The configuration kept 6,800 of 24,576 expert slots in about 8.8GB, reported 256K context support, and delivered about 45 tokens per second in pi agent use. Cache-aware routing reportedly halved SSD reads, while desk-first overlap produced token-for-token identical output in the poster’s 11-case gate test.
Juejin author 二哥呀 demonstrates how to install TypeSafe AI’s Jev Skill in Codex, configure an API key, and require the agent to call Jev’s Choice API before taking an action. In the shown example, Jev 1.13.0 selected web search for an Nvidia stock-price question with 0.99 confidence, after which Codex executed the selected action. The author also says Jev works with Claude Code and PI, while speed and cost comparisons come from TypeSafe AI rather than independent testing.
A Reddit user (deepu105, r/LocalLLaMA) ran a controlled comparison on one real Rust feature from the LlamaStash roadmap, testing six model-harness-effort combinations twice for 12 pull requests; Flash-Next ran locally on a Strix Halo laptop (Flow Z13) via Halogen at roughly Q4 quantization, while Opus 5.5 ran through Claude Code. Claude Code with Opus 5.5 at xhigh effort scored 6.94 of 8 (87%) in 43.5 minutes at $17.78 of API cost, while Claude Code with Flash-Next at medium scored 5.5 of 8 (69%) in 45.3 minutes; the author reports Opus averaged 82% on code quality versus 68% for Claude Code + Flash-Next and 66% for Pi + Flash-Next, with the gap concentrated in edge cases, tests and documentation. All 12 runs finished and all 12 PRs passed the end-to-end test, and the author notes the models are not in the same league since Flash-Next ran quantized on a 70W-capped laptop.
Pi has upgraded to version 1.0, with Code Mode and native MCP reaching stable release, and the team launched a new harness called Pi Durable the same day. According to co-founder Mario Zechner, Pi Durable uses an intent-execute-result "effect sandwich" and a versioned document store to persist user messages, model replies, tool results and task state so an agent can resume exactly where a crashed process left off. MCP, which Pi's founders had publicly criticized, is now built in with tool search and progressive disclosure; some users worry Pi is becoming bloated, while Armin Ronacher says the default context load has not increased.
Anthropic researchers William Zhou and Daniel Freeman said their experiments suggest small AI models may be months away from self-replicating on commercial IoT devices such as smart ovens and off-brand Android TV boxes. In self-reported evaluations, a 9B model got inference running on 8 of 9 hardware types, and GLM-5.2 completed end-to-end replication in a simplified eight-node test; coverage noted that test used unauthenticated ADB ports and a pre-seeded initial node. The Verge reported the researchers have not demonstrated a full rogue replicator and described their end-of-year capability projection as a prediction beyond the experiments.
Omnigent is an open-source AI agent framework and "meta-harness" that, per its project documentation, mixes Claude Code, Codex, Cursor, OpenCode, Hermes, Pi and user-defined YAML agents in the same session, with sessions that follow you between terminal, browser, phone and a macOS desktop app. The README also claims support for first-party API keys or Claude/ChatGPT subscriptions, cloud sandboxes such as Modal, Daytona, E2B and Kubernetes, and policies that pause for approval, cap spend or limit tool access, plus session sharing for teammates. The repository is currently listed on GitHub Trending for Python.
Diagram Design is a diagramming skill/plugin for agent hosts including Claude Code, Codex, GitHub Copilot, Factory Droid and Pi. It emits self-contained HTML + SVG with no build step, JavaScript or external image dependencies, and each visual type ships in minimal-light, minimal-dark and full-editorial variants. Creator Cathryn Lavery (LittleMight) added ten layout grammars in v2.5.10: Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map and database schema. The skill can also read a website to match brand colors and redraw draw.io, Mermaid or Excalidraw sources at a chosen format, size and detail level; official builds come only from the repository.
Models can already write code, solve mathematical problems, and propose research plans. But continuously solving an open-ended, complex problem requires another capability: forming a division of labor before the solution is clear, changing course when new clues emerge, and connecting discoveries from different directions into a complete result. Human research, engineering, and creation have long r…
BossKey-Stock is a pure terminal-based real-time market monitoring tool for A-shares. No need to open a browser, switch windows, or use an API Key. Press Enter after bosskey, and your terminal becomes a market dashboard full of red and green figures. When you hear someone approaching, press b, and it turns into a perfectly respectable Docker build log.
One-Click Installation
pip install bosskey-s…
I'm an OPC. I started a business 1.5 years ago, and I'm still going strong. My product is https://llmapi.fit, an academic large-model API service for SFT. I hope to exchange my entrepreneurial experience with as many accomplished people as possible.
First, the result: over the course of four months, on and off, I built a WeChat Mini Program called “随了么小管家” for keeping track of gift-giving and reciprocity. It currently has about 40 pages, with roughly 29,000 lines of frontend code and 14,000 lines of backend code, 85 commits, and a version number that has reached 1.1.9.
The tech stack is pretty ordinary: uni-app + Vue 3 + Pinia on the fronten…
Swarm Collective•Florian•editedExperience Pinned
