Alternatives
Products that do what Panoptès does
Async RAM-only data engine with AI self-healing & Web3 API
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- 2AA
2018 · github.com
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- 4MA
Hey HN, We’ve been heads-down building MOSS - a semantic memory layer that brings AI-powered search and personalization fully on-device (No cloud | No latency | No data leaving the user’s device) We just launched a live demo showing MOSS running entirely in-browser, performing lightning-fast semantic search over local in-browser VectorDB. This unlocks a new class of privacy-first, hybrid AI experiences that work even without a server connection. If you’re curious about: - how to run AI search right inside the browser - the technical challenges behind on-device vector search - why we believe…
2025 · twitter.com
- 5AI
Hi HN, I’m Sean, the founder of Ascend.io (https://www.ascend.io). I’m really excited to post here and announce the launch of Ascend.io, a radical new way of designing, scaling, and automating data pipelines. Ascend is the result of nearly 4 years of development effort for a team that is now 30-strong, and I would love for you to give it a test drive and let me what you think. I’ve felt this pain since I wrote my first MapReduce in 2004 (using Sawzall @ Google), and in the 15 years since, things have not improved at the pace of other parts of the technology ecosystem. When I went…
2019
- 6MG
I built a governed memory layer for AI assistants with deletion compaction, vector purge verification, tenant isolation, and audit evidence.
Jun 2026 · github.com
- 7CH
2017 · github.com
- 8MS
I’m not a software engineer or a genius — I just had a weird idea: What if memory wasn’t just stored as text or embeddings, but as symbolic, byte-level thoughts that could be passed between AIs? That idea became MemoryCore Lite: Encodes thoughts into lightweight bytecode Shares them across nodes via peer-to-peer sync Fully decentralized, no GPU needed Designed to evolve into its own AI knowledge mesh I just open-sourced the basic version here: github.com/ProToxicNinja/MemoryCore-Lite-Symbolic-Memory-Engine-for-AI It’s simple — but everything works. You can build better tokenizers,…
2025 · github.com
- 9TS
We have built Tarit as a hypervisor built from ground up for running AI agent and RL environments. It is based on rust-vmm and can be used as a replacement for firecracker. Firecracker was built to serve a different need of primarily serverless compute and hence does not have primitives like live snapshots without pausing the VM operations. We also provide a basic orchestrator that handles placement of the microVMs, creating clusters with HA, maintaining a warm pool of VMs, and takes care of setting up networking and monitoring. Our benchmarks on a metal instance shows an acquire VM from…
Jul 2026 · github.com
- 10SA
I built Syne because I was tired of AI assistants that forget everything after each conversation. Syne is a self-hosted AI agent framework where memory is a first-class citizen — stored as semantic vectors in PostgreSQL, searchable across millions of entries, and persistent forever. Key features: - Unlimited persistent memory with semantic search (pgvector) - Anti-hallucination: only stores user-confirmed facts, auto-deduplicates - Self-evolving: creates new abilities at runtime without restart - Multi-model: switch between Gemini, ChatGPT, Claude mid-conversation - True $0/month setup:…
Feb 2026
- 11SS
I'm developing a storage system for versioning data at the subfile level, especially well suited for SSDs due to its log-structured COW nature. It implements a novel versioning algorithm called sliding snapshot, a diff-algorithm which makes use of our stable record-identifiers and optionally hashes, another diff algorithm for importing similar XML-documents as a versioned resource as well as novel XPath axis to navigate not only in space, but also in time. Recently, I've implemented a higher level, asynchronous REST-API with Kotlin (Coroutines) and Vert.x in a seperate module. The system is…
2018
- 12RA
2017 · github.com
- 13AN
Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
- 14CE
Claude.ai artifacts can call the Anthropic API and have persistent storage (5MB via window.storage). I used these two capabilities to build a memory system modeled on how human memory actually works — salience scoring, forgetting curves, and sleep consolidation — all running inside a single React artifact with no external dependencies. Just add artifact to your chat and paste instructions into your personal preferences setting.
Feb 2026 · github.com
- 15AD
2025 · github.com
- 16SA
Hi HN, we’re Jessie and Eric. We’ve been baking away at Cakework (https://www.cakework.com/), which is a way to build async backends without needing to manage cloud infrastructure. Cakework is for operations that take time or more compute, like file processing, report generation, or machine learning. Devs write backends as Python functions and deploy them with our CLI. They use our client SDKs to make requests, get status, and get processing results. Each request runs with its own CPU and memory parameters in its own microVM, with no timeouts. Devs can query for failures and…
2023 · cakework.com
- 17AS
2020 · github.com
- 185L
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
- 19AA
2021 · supernormal.com
- 20SA
Feb 2026 · github.com
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- 23WB
Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
Mar 2026 · hive.rllm-project.com
- 24WM
May 2026 · github.com
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