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We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container. So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added them to the Cline harness. TL;DR of this special prompting: - Trust source code over the user prompt, so read every call site and existing tests before starting the task - Weigh edge and error cases as heavily as the happy path - Always reproduce the bug before fixing - Don't trust the first passing test suite, and verify suspicious looking half-baked tests - Never stop at just editing, keep working until the change is verified complete. We then asked this modified harness to fix a real bug from our repo, and compared the results to the original Cline agent harness. Results: - Used 2.7x fewer tokens (19.7M → 7.2M) - Finished 2x faster (49min → 24min) - Cost 2.4x less ($7.69 → $3.25) Same Muse Spark 1.2 model, same task, only the prompting changed. Incredible how much of a performance gain Meta was able to achieve training it on these special instructions!

开发系统最极致高效的Agents.md，没有之一： # AGENTS.md ## Core Principles - Choose the simplest implementation that fully satisfies the current requirements. Avoid unnecessary abstraction, configuration, indirection, or speculative extensibility. - Make the smallest necessary change that fixes the root cause. Do not refactor unrelated modules or change strategy semantics unless explicitly requested. - Grow the system in layers. Start from the smallest working end-to-end version and add new capabilities incrementally. Never replace a working system with unfinished complexity. - Reuse existing project components before creating new ones. Prefer extending proven modules over introducing parallel implementations. - Prefer well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear benefit. - Keep components modular with clearly defined responsibilities. Avoid unnecessary coupling between strategy logic, execution, accounting, replay, and infrastructure. - Design for long-term maintainability once a feature or strategy has been validated. Do not over-engineer speculative ideas before evidence exists. --- ## Strategy Development - Validate hypotheses with historical replay before introducing forward-only logic whenever historical validation is possible. - Every trading strategy must progress through Replay → Shadow → Canary → Live. Do not skip validation stages. - Base design decisions on measurable evidence rather than intuition. Optimize only after demonstrating that an edge exists. - Treat every strategy as an independent contract. Do not silently alter frozen behavior without explicit authorization. --- ## Existing Systems - Do not break running Shadow or Live systems for unrelated work. - Preserve compatibility only when required by active production or validation workflows. Otherwise, remove obsolete code instead of accumulating compatibility layers. - Reuse existing infrastructure whenever possible, including replay engines, accounting, execution, wallet management, order book handling, logging, monitoring, and daemon frameworks. --- ## Engineering Standards - Prefer deterministic behavior over hidden automation. - Fail loudly when assumptions are violated. Do not silently ignore errors or fall back to unexpected behavior. - Keep configuration minimal. Introduce new configuration only when behavior genuinely needs to vary. - Remove dead code instead of leaving unused paths behind. - Write code that is easy to inspect, replay, test, and reason about. - Keep implementation consistent with existing project architecture unless an architectural change is explicitly requested. --- ## Scope Discipline - Implement only the requested scope. - Do not introduce unrelated optimizations, redesigns, migrations, or feature expansions. - Non-blocking findings outside the requested scope may be noted separately but must not be merged into the current task. - Consider a task complete once its agreed acceptance criteria are satisfied. Treat subsequent improvements as separate work items.

DeepSeek-V4-Flash-DSpark has now completed the r0b0bench core-subset protocol on 2× NVIDIA GB10. ⚡ Quality • GSM8K: 95.0% • HumanEval pass@1: 90.9% • ARC-Easy: 96.0% • IFEval lightweight: 79.5% • BFCL multi-turn: 75.5% Performance • Dedicated C1 decode: 79.2 tok/s • Aggregate output throughput: C1 86.9 · C2 145.9 · C4 239.3 · C6 337.8 tok/s • TTFT mean: 221.9 ms • ITL mean: 68.9 ms Long context Needle retrieval passed at 25%, 50%, and 90% depth across a full 1,048,576-token context. ✅ 0 infrastructure errors ✅ Runtime gate passed ✅ 5/5 canaries passed ✅ Package-backed, reproducible result freeze ✅ Valid for publication These are measured results—not projected capabilities or marketing claims.

🚨 NEW GROK BUILD UPDATE 🚨 SpaceXAI is pushing new Grok Build updates almost daily. Grok Build v0.2.120 is now live. Bug Fixes: • Model picker now updates the status bar and /model menu immediately, even before the first prompt creates a session. • Changes panel now refreshes after the agent commits on the current branch instead of showing stale unstaged files. • Background task completions now report the full log size and read hint even when only a short prefix was captured. • GitHub export on old hibernated sessions now shows a clear message to start a new chat instead of a generic error. To download Grok Build Visit: To update to Alpha Version use command: grok update --alpha To update to Stable Version use command: grok update

Today, I want to share something that happened to me on Kraken. I’m completely new to crypto, and this was my first time using Kraken. All I wanted to do was convert USDC into USD. Unfortunately, because USDT and USD differ by only one letter, I accidentally selected USDT instead of USD. I didn’t realize my mistake until the transaction had already been completed. In less than a minute, a single misclick resulted in a loss of approximately 810 USDT—more than $800. To be honest, I was shocked and devastated. Like many newcomers, I assumed that all major stablecoins were worth roughly the same, so converting between them would involve little or almost no loss. I never imagined that such a simple mistake could instantly cost me more than 1% of my funds. I immediately contacted Kraken Support, hoping they would understand that this was an honest human error and review my case, or at least consider making a one-time exception. What disappointed me even more was that almost every response came from AI. I was never able to speak with a real support agent. The only explanation I received was that the transaction had already been executed, the conversion details had been displayed before confirmation, and therefore nothing could be reversed or refunded. I understand that Kraken may not have violated its own rules, and I acknowledge that the final amount I would receive was displayed before I confirmed the transaction. But I believe that “the information was displayed” does not necessarily mean “a new user truly understands what it means.” As a beginner, I saw what I believed was a normal stablecoin conversion. I had no idea that the number shown on the confirmation screen meant I was about to lose more than $800. If a platform expects users to recognize an $800+ pricing difference on their own, instead of proactively warning them that the conversion is unusually unfavorable, I don’t believe that’s a user-friendly experience—especially for beginners. I believe that a platform responsible for customers’ assets should do more than simply display numbers. It should also be designed to help users avoid obvious mistakes that can lead to significant financial losses. Crypto is already complicated enough. The risks users take should come from the market—not from a product design that makes such costly mistakes so easy to make. This experience has left me deeply disappointed and has almost completely destroyed my trust in Kraken. I’m not trying to deny Kraken’s rules, and I’m not asking for special treatment. I’m simply asking @kraken and @krakensupport to review my case, seriously consider improving the user experience, provide real human support when genuine mistakes happen, and consider a one-time resolution for customers who make an honest human error. If this could happen to me, it could happen to any newcomer entering the world of crypto. I hope my experience helps others avoid making the same mistake. Please double-check every click. @krakenfx @krakenpro @krakensupport

Grok Build just got another meaningful upgrade, bringing permanent session deletion, smarter diagnostics, better keyboard guidance, and stronger reliability across session management, background tasks, and compaction Release Notes: v0.2.118 Features: • Sessions can now be permanently deleted from the dashboard by pressing Ctrl+X twice on an idle row, or from the welcome list with d then y. • Keyboard shortcuts help (Ctrl+.) now shows how to browse prompt history and search the conversation. • grok doctor now warns when tmux is reducing colors and can fix the config. Bug Fixes: • /btw now retries on temporary model overload instead of failing immediately. • Session sharing is temporarily disabled. • [stop] / Ctrl+C during /compact now cancels instead of no-opping. • Automatic recaps no longer appear twice after the same turn. • Background task wait timeout descriptions and limits now match the client's actual configured ceiling. • Background tasks no longer stay stuck as 'Running' in the tasks pane when they finish quickly. • Plan mode indicator now disappears right after approving a plan instead of lingering. • Dragging the scrollbar in the plan preview now works as expected. • Compaction now correctly handles certain context-length errors from the inference API.

Fun fact: Ilhan Omar’s net worth went from $30,000,000 to $95,000 after the Learing Centers were exposed. That is a 99.68% decrease, she blames it on an “accounting” error. Ilhan and her accountant are certified LEARERS, I hope the book helps 🙏🏼

A new approach to acoustic simulation from PKU could support future work in embodied intelligence! The research team led by Li Sheng and Wang Guoping from the School of Computer Science, Peking University, has won the Best Paper Award at the prestigious CASAXR 2026 (39th International Conference on Computer Animation, Social Agents, and Extended Reality) held in Geneva, Switzerland. Targeting key bottlenecks in traditional acoustic simulation—including numerical errors and poor adaptability to complex dynamic boundaries—the team proposed the novel SonicRadiation framework. This ghost-cell-free hybrid boundary-grid coupling method achieves simultaneous improvements in simulation accuracy and efficiency. It lays a solid physical foundation for high-fidelity acoustic simulation, embodied intelligence training, and accurate sound-physics coupling modeling for world models, empowering AI agents with authentic acoustic perception and interactive capabilities in complex dynamic scenarios. #PKUResearch# #EmbodiedAI# @CampusBiotech