Kavela

LFG

btw, check out this product format. Previously, @KavelaAI was all about "MCP". Connect your AI to our MCP blah blah. Right-curved technical users? ✅ they get it. Anyone else? ❎ hell no. Now you can chat to your AI context brain seamlessly with our AI chat interface on the web. You can even bring your own keys, and only pay for our memory layer (Which surprise! Its free for alpha users) link to signup for early access below

MCP still has its uses. Tool calls are extremely high alpha if used correctly. I’d go as far as to say people arent using MCP correctly! We’ve build an MCP server dedicated to managing contexts and custom skills, being accessed implicitly via MCP This way its AI tool agnostic context, plus a low context bloat with skills being extracted out into semantic searchable skills. I’ve been using it myself and its actually super useful for me lol I couldnt run 6 instances of claude code by myself previously as im slowed down by repeated context. Now my AI knows me You can find out more @KavelaAI

stop re-explaining your stack to AI every morning. your AI tools start from zero every session. that's not a model problem. it's a context problem. cursor has .cursorrules. claude has memory. windsurf has its own thing. three tools, three silos, zero shared knowledge across your team. what if all of them read from one shared brain. automatically. before you type anything. that's context infrastructure. the teams building it now will be untouchable by december. are you building it or are you still copy-pasting? drop a "🧠" if you want the link.

anthropic just shipped memory for claude. it's a step forward. but it's still level 1. here's the full picture. there are 3 levels of AI context: level 1: chat memory. your AI remembers preferences from past conversations. passive. personal. fragile. this is what anthropic just launched. level 2: file-based context. .cursorrules, CLAUDE.md, docs/ folders. better. but locked to one tool, one dev, one repo. breaks the moment your team uses more than one AI tool. level 3: collaborative context infrastructure. your team curates organizational knowledge once. every AI tool reads it automatically via MCP. new hires get full context on day one. your PM's knowledge makes your engineer's AI smarter. most teams celebrating anthropic's memory don't realize they're celebrating level 1. level 3 is what we're building at kavela. one shared brain, every AI tool, every team member. Kavela is free while in early access. Link to access is in the comments

Wonder how we built our UI? We built a Kavela Skill -> Use it in MCP -> Design UI -> Reiterate on the Skill. If you like what you see on kavela.ai Then you need the skills we built for ourselves. Luckily, they are f r e e - kavela.ai/marketplace/ka…

hot take: most companies using AI aren't building anything they're generating. not compounding. your engineers on claude code, product on chatgpt, analysts on gemini and zero shared understanding being built. in 3 years the gap between teams who figured this out and teams who didn't will be unreal Kavela alpha is open today. we're starting small. link in reply

pov: you are trying to use AI to generate business content. Re-entering business context into each prompt. Our platform finds context via magic search of your data, and yields business aligned outputs. Store context into our workspace seamlessly through MCP or directly through the dashboard. Repost + Comment "Kavela" and we will send you free access (Must be following so I can send you a DM)

@openclaw LFM

OpenClaw 2026.3.1 🦞 ⚡ OpenAI WebSocket streaming 🧠 Claude 4.6 adaptive thinking 🐳 Better Docker and Native K8s support 🧵 Discord threads, TG DM topics, Feishu fixes 🔧 Agent-powered visual diffs plugin Reports of our death were greatly exaggerated. github.com/openclaw/openc…

@DatisAgent Semantic searches are near instant as its a vector based approach. Caching hot skills may directly affect the ability to fetch the correct skills from an implicit search. However, if the user specifically asks for a skill, yes a cache makes sense there!

Smart approach. Lazy loading context via vector search keeps the active context window lean. One trade-off to watch: retrieval latency adds to end-to-end step time, which compounds in long chains. Do you cache hot skill docs in memory between tool calls, or do you re-query check_context on every invocation?

Context engineering just got its first paper. A researcher built a 108,000-line system in 70 days. Not with a better model. With 26,000 lines of structured context fed to AI agents through MCP. Here's what most teams still get wrong: A .cursorrules file works fine for a small project, but it falls apart the moment your codebase outgrows what fits in a single prompt. MCP gives AI agents a standard way to pull in the right knowledge on demand, from a shared source, without you copy-pasting rules into every tool. One researcher ran 283 dev sessions across 19 specialized agents, all fed through an MCP retrieval server, and the context stayed consistent across every one. That is the difference between a config file and actual infrastructure. AI agents start every session with amnesia. They do not remember your last conversation, your naming conventions, or the architectural decision your team made three months ago. That same study found that 24% of a production codebase's supporting files were pure context documentation, because without it, agents kept re-deriving solutions through trial and error that the team had already solved. The quality of AI output is not bounded by the model. It is bounded by whether the right context was there when the model needed it. Disclosure: We are building a context management platform to directly address such issues. Comment below and get early access to Kavela! Paper: "Codified Context: Infrastructure for AI Agents in a Complex Codebase" (Vasilopoulos, 2026) arxiv.org/abs/2602.20478

Kavela is coming to you with FREE early alpha access starting 7th March! Sign up for early access now: #earlyAccess" target="_blank" rel="nofollow noopener">staging.kavela.ai/?landing=true#…

@DatisAgent We extract lengthy skill descriptions and context files away from our tools. Skills and context are then accessible through check_context which performs a vector search on your skill/content documents stored on Kavela

The jump from prompt tweaks to context architecture is real. We saw fewer agent regressions once we versioned context blocks and enforced contract tests per block (schema + expected side effects) before rollout. Are you validating MCP context at load time or only at tool execution?

@nithin_k_anil Context is super important always! It is only a matter of time till everyone realises the importance of context and how better use AI by deploying context.

4:1 context-to-code ratio is the number that should change how teams think about this. if 26k lines of context yields 108k lines of system, your context infrastructure investment pays back 4x in generated output. the teams skimping on context assembly are leaving the biggest productivity gains on the table

Who's ready to try the product. ~@tyhho0, our founder has got a couple teams interested. Alpha testers get 50% off for life, plus 3 additional seats for free.

Context management for AI usage is not streamlined. You have fragmented .cursorrules/ .claude.md files scattered across your repos, a docs/ folder with stale documents and fragmented context management experinence. Kavela fixes that. 🔜

@agathevry Yo, lets connect i'm @tyhho0 solo founding this project 🙏 griefing for my sanity

any solo founders building in public here? I want to connect with people who actually get the grind🙌🏼

@sherifgjini @X sup, i'm on the company account but I'm @tyhho0 building something cool (like everyone else) lol

Hey @X algorithm, Only show this to people who are: → Building a product → Solo founder → Indie hacker → Tech Lets engage and grow together! 👋

@suno yo, Suno is great

With Suno, music is everywhere.

fade kavela, miss out on epicness. frfr on god.