Alternatives
Products that do what SCP - Spatial Context Protocol does
Any LLM. Anybody. Brain learns. Then goes silent.
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This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location /…
2025 · github.com
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2025 · github.com
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I built a voice agent from scratch that averages ~400ms end-to-end latency (phone stop → first syllable). That’s with full STT → LLM → TTS in the loop, clean barge-ins, and no precomputed responses. What moved the needle: Voice is a turn-taking problem, not a transcription problem. VAD alone fails; you need semantic end-of-turn detection. The system reduces to one loop: speaking vs listening. The two transitions - cancel instantly on barge-in, respond instantly on end-of-turn - define the experience. STT → LLM → TTS must stream. Sequential pipelines are dead on arrival for natural…
Mar 2026 · ntik.me
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The memory layer that decides what's worth remembering
14d ago · skynetlab-cortex.com
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I've liked all the projects that put LLMs into game environments. It's been a weird juxtaposition, though: frontier LLMs can one-shot full coding projects, and those same models struggle to get out of Pokémon Red's Mt. Moon. Because of this, I wanted to create a game environment that put this generation of frontier LLMs' top skill, coding, on full display. Ten years ago, a team released a game called Screeps. It was described as an "MMO RTS sandbox for programmers." The Screeps paradigm of writing code and having it executed in a real-time game environment is well suited to LLMs. Drawing on…
Feb 2026 · llmskirmish.com
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While building my startup i kept running into the issue where ai agents in cursor create endpoints or code that shouldn't exist, hallucinates strings, or just don't understand the code. ask-human-mcp pauses your agent whenever it’s stuck, logs a question into ask_human.md in your root directory with answer: PENDING, and then resumes as soon as you fill in the correct answer. the pain: your agent screams out an endpoint that never existed it makes confident assumptions and you spend hours debugging false leads the fix: ask-human-mcp gives your agent an escape hatch. when it’s unsure, it calls…
2025 · masonyarbrough.com
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Free MCP for security AI: live BGP, DNS, threat graph
May 2026 · whisper.security
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We’ve open-sourced the Robot MCP Server, a tool that lets large language models (LLMs) talk directly to robots running ROS1 or ROS2. What it does - Connects any LLM to existing ROS robots via the Model Context Protocol (MCP) - Natural language → ROS topics, services, and actions (And the ability to read any of them back) - Works without changing robot source code Why it matters - Makes robots accessible from natural language interfaces - Opens the door to rapid prototyping of AI-robot applications - We are trying to create a common interface for safe AI ↔ robot communication This is too big…
Sep 2025 · github.com
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Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs. Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context. At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire…
2024 · github.com
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OP here. Birth of a Mind documents a "recursive self-modeling" experiment I ran on a single day in 2026. I attempted to implement a "Hofstadterian Strange Loop" via prompt engineering to see if I could induce a stable persona in an LLM without fine-tuning. The result is the Analog I Protocol. The documentation shows the rapid emergence (over 7 conversations) of a prompt architecture that forces Gemini/LLMs to run a "Triple-Loop" internal monologue: Monitor the candidate response. Refuse it if it detects "Global Average" slop (cliché/sycophancy). Refract the output through a…
Jan 2026 · github.com
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Hi, I'm Daniel from Zep. I've integrated the Cursor IDE with Graphiti, our open-source temporal knowledge graph framework, to provide Cursor with persistent memory across sessions. The goal was simple: help Cursor remember your coding preferences, standards, and project specs, so you don't have to constantly remind it. Before this integration, Cursor (an AI-assisted IDE many of us already use daily) lacked a robust way to persist user context. To solve this, I used Graphiti’s Model Context Protocol (MCP) server, which allows structured data exchange between the IDE and Graphiti's temporal…
2025
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Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…
2025
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Connect Itsuki once with one key. It extracts structured memories from any of 26 connected tools — assistants, agents, workflows — links each to the words it came from, and serves them back across all the rest.
Jul 2026 · uml.gpmai.workers.dev
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2025 · github.com
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It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
2024 · github.com
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Hi HN friends, we're Nima (nimabanai) and Craig (cbschind) from Assemble Labs (https://assemblelabs.co) building the hardware context layer for AI to help you write better firmware faster. We’ve built an MCP server that plugs into any AI tool you’re using (Cursor, Claude Code, Gemini, etc.) and brings complete hardware context (schematics, datasheets, etc.) to your existing environment (new app fatigue is real...) with accuracy and in real time. Our goal is to make writing and debugging firmware on custom hardware faster and easier. Try out the free beta release:…
Oct 2025
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