Google's research shows that LLMs experience difficulty recalling facts when questions reverse the usual subject/object entity order.
Serdar Yegulalp writes that he tested PrismML'''s Bonsai 27B 1-bit quantized model locally on an RTX 5060 with LM Studio, finding it compact enough for a smartphone but slower than smaller models due to its 27 billion parameters. Despite modest token-per-second speeds, the model delivers strong reasoning and coding quality when thinking is enabled, making it a good trade-off for users prioritizing small footprint and large context over speed.
- 1-bit quantized version is 3.9 GB versus 54 GB original
- Maximum context window is 262,144 tokens; test used 32,767
- Supports speculative decoding and flash attention with open Apache 2.0 weights
- Speculative decoding not usable on 8GB VRAM due to draft model requirement
- Tokenization of code examples took up to two minutes, max output ~40 tokens/sec
- 1-bit quantized version is 3.9 GB versus 54 GB original
- Maximum context window is 262,144 tokens; test used 32,767
- Supports speculative decoding and flash attention with open Apache 2.0 weights
- Speculative decoding not usable on 8GB VRAM due to draft model requirement
- Tokenization of code examples took up to two minutes, max output ~40 tokens/sec
Michael Kozlowski writes that a long-standing workaround for extending Libby/OverDrive loans on Kindle by enabling Airplane Mode no longer works after a recent software update, with expired library books now closing when the device reconnects to Amazon. The exploit is broken on Kindle Paperwhite 11th and 12th generations, Colorsoft Signature Edition and devices on firmware 5.19.5, while older firmware 5.19.2 and below may still allow it, though results vary.
- Reddit thread r/kindle shows dozens of hardcore users confirming the loss of the exploit
- Executives at Amazon and OverDrive reportedly tried for years to stop the airplane mode loan extension
- No official comment has been made and Libby or Amazon library lending pages have not been updated
- Reddit thread r/kindle shows dozens of hardcore users confirming the loss of the exploit
- Executives at Amazon and OverDrive reportedly tried for years to stop the airplane mode loan extension
- No official comment has been made and Libby or Amazon library lending pages have not been updated
SWE-bench Verified is a human-filtered subset of 500 instances from SWE-bench created in collaboration with OpenAI to provide a reliable evaluation set for coding agents and language models. Human annotators reviewed each instance to ensure problem descriptions are clear, test patches are correct, and tasks are solvable with available information. The Verified leaderboard shows results from a wide variety of AI coding systems, and a Bash Only view isolates language model performance using mini-SWE-agent in a minimal bash environment.
- Human validation ensures clarity, correctness of test patches, and solvability
- Bash-only comparison uses mini-SWE-agent with ReAct loop, no tools or scaffold
- Leaderboard distinguishes mini-SWE-agent LM results from full agent systems via Agent dropdown
- Release 2.x uses tool calling vs 1.x parses actions from output strings, making releases not directly comparable
- Human validation ensures clarity, correctness of test patches, and solvability
- Bash-only comparison uses mini-SWE-agent with ReAct loop, no tools or scaffold
- Leaderboard distinguishes mini-SWE-agent LM results from full agent systems via Agent dropdown
- Release 2.x uses tool calling vs 1.x parses actions from output strings, making releases not directly comparable
Kanwal Mehreen writes that as large language models move from chatbots to coding agents and autonomous workflows, good prompting is no longer enough and specification engineering is emerging as the key skill. It is the practice of defining goals, constraints, expected outputs, edge cases, tests, success criteria and failure modes so AI-assisted work is testable and reviewable. Prompt engineering asks how to ask, while specification engineering defines what done correctly means, a shift closer to product management, testing and research design than traditional prompting.
- A 2024 Requirement-Oriented Prompt Engineering paper found ROPE training improved requirement-writing ability by 20% versus 1% for conventional prompt engineering training.
- OpenAI Structured Outputs, Model Spec and Anthropic Constitution exemplify specification engineering at API and model-behavior levels.
- SWE-bench Verified is a human-validated subset used to evaluate real-world software issue solving with agents.
- Google's 2025 DORA report surveyed nearly 5,000 tech professionals and found AI amplifies existing organizational strengths and weaknesses.
- A 2024 Requirement-Oriented Prompt Engineering paper found ROPE training improved requirement-writing ability by 20% versus 1% for conventional prompt engineering training.
- OpenAI Structured Outputs, Model Spec and Anthropic Constitution exemplify specification engineering at API and model-behavior levels.
- SWE-bench Verified is a human-validated subset used to evaluate real-world software issue solving with agents.
- Google's 2025 DORA report surveyed nearly 5,000 tech professionals and found AI amplifies existing organizational strengths and weaknesses.
OpenSandbox is a general-purpose sandbox platform for AI applications offering multi-language SDKs, unified sandbox APIs and Docker/Kubernetes runtimes for use cases like coding agents, GUI agents, evaluation, code execution and RL training. It provides SDKs, CLI and MCP integration, a sandbox protocol for custom runtimes, built-in environments such as command, filesystem and code interpreter, network ingress/egress controls, credential vault injection and strong isolation via gVisor, Kata Containers and Firecracker.
- Release images published to Docker Hub, GHCR and Alibaba Cloud with Cosign signatures and provenance
- SDKs for Python, Java/Kotlin, TypeScript/JavaScript, C#/.NET and Go
- OpenSSF Best Practices badge and CNCF Landscape listing
- Release images published to Docker Hub, GHCR and Alibaba Cloud with Cosign signatures and provenance
- SDKs for Python, Java/Kotlin, TypeScript/JavaScript, C#/.NET and Go
- OpenSSF Best Practices badge and CNCF Landscape listing
Traceway is an OpenTelemetry-native observability platform that combines logs, traces, metrics, session replay, exceptions, AI tracing and on-call paging in one MIT-licensed tool that can be self-hosted with Docker in about 90 seconds or run embedded inside a Go app.
- Provides DuckDB telemetry backend for self-hosted mode and ClickHouse + PostgreSQL for standalone deployments
- Ships agent-first CLI and SKILL.md skills for Claude Code, Cursor and Codex to set up and query telemetry
- Symbolicator resolves minified production errors via memory-mapped .tw files with sub-millisecond lookups
- Provides DuckDB telemetry backend for self-hosted mode and ClickHouse + PostgreSQL for standalone deployments
- Ships agent-first CLI and SKILL.md skills for Claude Code, Cursor and Codex to set up and query telemetry
- Symbolicator resolves minified production errors via memory-mapped .tw files with sub-millisecond lookups
Hee-Suk Kim writes that Sib is a standard Unix LLM client which stores LLM conversations as a plain Git repository where each user and assistant turn is a commit, providing full context control and free backup and sharing via normal Git remotes. The `sib ask` command reads a prompt from stdin, appends it with the reachable chain from HEAD, records both turns as commits, and prints the reply, while flags like -p, -r, -c enable repeat, edit, fork and head moves without API calls.
- Dependencies are bash >=3.2, git, jq, curl, awk and coreutils
- Conversations can be fetched/pushed via `sib git` to share with sib-project/hub
- Dependencies are bash >=3.2, git, jq, curl, awk and coreutils
- Conversations can be fetched/pushed via `sib git` to share with sib-project/hub
xiaowuc2 writes that the qxresearch-event-1 repository provides a hands-on collection of 50+ concise Python applications averaging about ten lines of code each, spanning Machine Learning, Deep Learning, GUI, Computer Vision and API development, with video explanations on YouTube and guidance for learning, experimenting and customization.
Termaid is a pure Python library and CLI that renders Mermaid diagrams as Unicode or ASCII art directly in the terminal or within Python apps, supporting 18 diagram types with zero dependencies, terminal-aware auto-fitting, optional Rich colored output and Textual widget integration.
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
- Inspired by mermaid-ascii and beautiful-mermaid
- Offers 11 built-in themes including gruvbox, monokai, dracula, nord and solarized
- Pipe-friendly CLI examples include `cat diagram.mmd | termaid` and `uvx termaid diagram.mmd`
