Show HN: Gemini Plugin for Claude Code https://ift.tt/bEOALK7

Show HN: Gemini Plugin for Claude Code I built a plugin that lets Claude Code delegate work to Gemini CLI. I started this after finding myself reaching for Gemini more often on long context repo work. I have been especially liking Gemini’s codebaseinvestigator for long context. This is inspired by openai/codex-plugin-cc. Code Review, adversarial review. Under the hood its Gemini CLI over ACP Would love feedback from people using Claude Code, Gemini CLI, or ACP. I am especially curious whether this feels useful outside my own workflow. Its a great combo with Opus 4.7 + Gemini 3.1 workflows https://ift.tt/my1DKkq April 22, 2026 at 01:41AM

Show HN: MemFactory: Unified Inference and Training Framework for Agent Memory https://ift.tt/wh4qfHG

Show HN: MemFactory: Unified Inference and Training Framework for Agent Memory Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operations, such as extraction, updating, and retrieval, has emerged as a highly promising research direction. However, existing implementations remain highly fragmented and task-specific, lacking a unified infrastructure to streamline the integration, training, and evaluation of these complex pipelines. To address this gap, we present MemFactory, the first unified, highly modular training and inference framework specifically designed for memory-augmented agents. Inspired by the success of unified fine-tuning frameworks like LLaMA-Factory, MemFactory abstracts the memory lifecycle into atomic, plug-and-play components, enabling researchers to seamlessly construct custom memory agents via a "Lego-like" architecture. Furthermore, the framework natively integrates Group Relative Policy Optimization (GRPO) to fine-tune internal memory management policies driven by multi-dimensional environmental rewards. MemFactory provides out-of-the-box support for recent cutting-edge paradigms, including Memory-R1, RMM, and MemAgent. We empirically validate MemFactory on the open-source MemAgent architecture using its publicly available training and evaluation data. Across the evaluation sets, MemFactory improves performance over the corresponding base models on average, with relative gains of up to 14.8%. By providing a standardized, extensible, and easy-to-use infrastructure, MemFactory significantly lowers the barrier to entry, paving the way for future innovations in memory-driven AI agents. https://ift.tt/hDjMl8f April 21, 2026 at 11:26PM

Show HN: A fake small claims court for petty complaints https://ift.tt/wtRk5NY

Show HN: A fake small claims court for petty complaints https://ift.tt/efwMzxP April 21, 2026 at 01:04AM

Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness https://ift.tt/Jwot548

Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution. Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations. A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements. This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to. An article with more technical detail: https://ift.tt/uDECh0M https://mediator.ai/ April 20, 2026 at 12:07PM

Show HN: Palmier – bridge your AI agents and your phone https://ift.tt/BreA43l

Show HN: Palmier – bridge your AI agents and your phone Hi HN — I built Palmier. Palmier bridges your AI agents and your phone. It does two things: 1. It lets you use your phone to directly control AI agents running on your computer, from anywhere. 2. It gives your AI agents access to your phone, wherever you are — including things like push notifications, SMS, calendar, contacts, sending email, creating calendar events, location, and more. A few details: * Supports 15+ agent CLIs * Supports Linux, Windows, and macOS * What runs on your computer and your phone is fully open source * Works out of the box — no need to set up GCP or API keys just to let agents use phone capabilities * Your phone can act as an agent remote: start tasks, check progress, review results, and respond to requests while away from your desk * Your phone can also act as an agent tool: agents can reach into phone capabilities directly when needed * Optional MCP server: if you want, Palmier exposes an MCP endpoint so your agent can access phone capabilities as native MCP tools. This is optional — you can also use Palmier directly from the phone app/PWA, with those capabilities already built in * Still in alpha stage, with bugs. Opinions and bug reports very welcome The basic idea is that AI agents become much more useful if they can both: * interact with the device you actually carry around all day * be controlled when you are away from your computer Palmier is my attempt at that bridge. It already works with agent CLIs like Claude Code, Gemini CLI, Codex CLI, Cursor CLI, OpenClaw, and others. You can run tasks on demand, on a schedule, or in response to events. Would especially love feedback on: * whether this feels genuinely useful * which phone capabilities are most valuable * which agent CLIs I should support next * what feels broken, awkward, or confusing Site: https://www.palmier.me Github: * https://ift.tt/KD5g0XR * https://ift.tt/zsZqDLM Happy to answer questions. https://ift.tt/KD5g0XR April 20, 2026 at 11:31PM

Show HN: Self-hosted Raspberry Pi wall display (no cloud, no subscription) https://ift.tt/ORjL2Yf

Show HN: Self-hosted Raspberry Pi wall display (no cloud, no subscription) https://ift.tt/k9Zc3J1 April 20, 2026 at 11:13AM

Show HN: CyberWriter – a .md editor built on Apple's (barely-used) on-device AI https://ift.tt/xat6Pbw

Show HN: CyberWriter – a .md editor built on Apple's (barely-used) on-device AI Apple has quietly shipped a pretty complete on-device AI stack into macOS, with these features first getting API access in MacOS 26. There are multiple components in the foundation model, but the skills it shipped with actually make this ~3b parameter model useful. The API to hit the model is super easy, and no one is really wiring them together yet. - Foundation Models (macOS 26) - a ~3B-parameter LLM with an API. Streaming, structured output, tool use. No API key, no cloud call, no per-token cost. - NLContextualEmbedding (Natural Language framework, macOS 14+) -- a BERT-style 512-dim text embedder. Exactly what OpenAI and Cohere sell, sitting in Apple's SDKs since iOS 17. - SFSpeechRecognizer / SpeechAnalyzer - on-device speech-to-text including live dictation. Solid accuracy on Apple Silicon. I built cyberWriter, a Markdown editor, on top of all three, mostly as a test and showcase to see what it can do. I actually integrated local and cloud AI first, and then Apple shipped the foundation model, it stacked on super easy, and now users with no local or API AI knowledge can use it with just a click or two. Well the real reason is because most markdown editors need plugins that run with full system access, and I work on health data and can't have that. Vault chat / semantic search. The app indexes your Markdown folder via NLContextualEmbedding (around 50 seconds for 1000 chunks on an M1). The search bar gets a "Related Ideas" section that matches by meaning - typing "orbital mechanics" surfaces notes about rockets and launch windows even when those exact words never appear. Ask the AI a question and it retrieves the top 5 chunks as context. Plain RAG, but the embedder, retrieval, chat model, and search all run locally. AI Workspace. Command+Shift+A opens a chat panel, Command+J triggers inline quick actions (rewrite, summarize, change tone, fix grammar, continue). Apple Intelligence is the default; Claude, OpenAI, Ollama, and LM Studio all work if you prefer. The same context layer - document selection, attached files, retrieved vault chunks - feeds every provider through the same system-message path. Because the vault context is file and filename aware, it can create backlinks to the referenced file if it writes or edits a doc for you. Voice notes and dictation. Record a voice note directly into your doc, transcribe it with SpeechAnalyzer, or just dictate into the editor while you think. Audio never leaves the Mac. The privacy story is straightforward because the primitives are already private. Vectors live in a `.vault.embeddings.json` file next to your vault, never sent anywhere. If you use Apple Intelligence, even the retrieved text stays on-device. For cloud models there is a clear toggle and an inline warning before any filenames or snippets leave the machine. Honest limitations: - 512-dim embeddings are solid mid-tier. A GPT-4-class embedder catches subtler relationships this will miss. - 256-token chunks can split long paragraphs mid-argument. - Foundation Models caps its context window around 6K characters, so vault context is budgeted to 3K with truncation markers on the rest. - Multilingual support is English-only right now. NLContextualEmbedding has Latin, Cyrillic, and CJK model variants; wiring the language detector across chunks is Phase 2. The developer experience for these APIs is genuinely good. Foundation Models streams cleanly, NLContextualEmbedding downloads assets on demand and gives you mean-poolable token vectors in a handful of lines. Curious what others here are building on this stack - feels like low-hanging fruit that has been sitting there for a while. https://ift.tt/BnwZyqt The Apple AI embedding feature is going live today. I'm honestly surprised it even works out of the box. https://cyberwriter.app April 20, 2026 at 10:07AM