If exact search works, keep it.
If semantic recall is poor, add embeddings.
If questions repeatedly cross entities and relationships, add a graph.
If agents repeat mistakes across sessions, add durable decision memory.
If people cannot explain why an answer used a source, fix provenance before adding another model.
What is TCREI framework
[Why Prompt Engineering Is the Most Important AI Skill You're ...](https://medium.com/@CobiOneCanobi/why-prompt-engineering-is-the-most-important-ai-skill-youre-not-learning-7432dc9e7aff)
[Another Prompting Framework: RACE (Role, Action, Context ...](https://drayseozturk.org/2025/02/22/another-prompting-framework-race-role-action-context-execute/)
[What is the TCREI Framework? A 5-Step System for Better AI ...](https://canadianaiguy.ca/definition/tcrei-framework/)
[How to Craft Effective AI Prompts with T-C-R-E-I Framework ...](https://www.linkedin.com/posts/mona--ebrahimi_generativeai-aiprompting-businessanalysis-activity-7351077167629225988--Jq2)
[How to Write Better Prompts for AI: Google's TCREI Framework ...](https://www.linkedin.com/posts/nakohamen-ekeoba_your-prompts-fail-because-you-skip-the-framework-activity-7379755442924830720-pPye)
The TCREI Framework is a five-step prompting system used to structure inputs for Large Language Models (LLMs) and get high-quality, consistent results. [1, 2]
The acronym stands for: Task, Context, References, Evaluate, and Iterate. [2]
------------------------------
## The 5 Components of TCREI## 1. T — Task
* What it means: Clearly define the core action you want the AI to perform. [3]
* Details to include: Specific command verbs, assigned personas or roles, and the exact output format (e.g., bullet points, a short table, or a structured report). [3, 4]
* Example: "Write a punchy LinkedIn post for a leadership coach targeting first-time tech managers." [5]
## 2. C — Context
* What it means: Provide the background story, target audience, goals, and constraints. [3, 5]
* Details to include: Who the audience is, why you are writing, what problem you are trying to solve, and what has been tried before. [4]
* Example: "They struggle with confidence, delegation, and communication." [5]
## 3. R — References
* What it means: Share examples, source materials, or style guides to steer the AI's tone and structure.
* Details to include: Few-shot examples (2 to 5 sample outputs), links, documents, or data sets. [4]
* Example: "Follow the tone of modern expert-led posts: short, story-driven, and ending with a question." [5]
## 4. E — Evaluate
* What it means: Review the AI's generated response critically.
* Details to include: Check for accuracy, missing details, tone alignment, and whether it fulfilled the task instructions. [5, 6]
## 5. I — Iterate
* What it means: Refine your prompt or follow up based on your evaluation.
* Details to include: Add more constraints, break long prompts into smaller steps, try different phrasing, or ask the AI to suggest alternative options (like a new hook or layout). [5, 6, 7]
------------------------------
If you are working on a prompt right now, share your goal/task and I can help you rewrite it using the TCREI framework.
[1] [https://canadianaiguy.ca](https://canadianaiguy.ca/definition/tcrei-framework/)
[2] [https://drayseozturk.org](https://drayseozturk.org/2025/02/05/prompting/)
[3] [https://aiwithkatarina.com](https://aiwithkatarina.com/googles-tcrei-prompting-framework/)
[4] [https://www.youtube.com](https://www.youtube.com/watch?v=wYeG0HzqTg8&t=325)
[5] [https://www.linkedin.com](https://www.linkedin.com/posts/ifioknkem7_heres-your-step-by-step-guide-to-writing-activity-7432861187416997888-bmDh)
[6] [https://community.atlassian.com](https://community.atlassian.com/learning/path/get-the-most-out-of-rovo/course/best-practices-for-using-rovo/lesson/best-practices-for-writing-ai-prompts)
[7] [https://www.linkedin.com](https://www.linkedin.com/posts/nakohamen-ekeoba_your-prompts-fail-because-you-skip-the-framework-activity-7379755442924830720-pPye)
Hermes OKF gives your AI agent a persistent, structured, version-controlled memory — no database, no lock-in, just markdown + YAML on your filesystem. Every decision, observation, and project context lives in a human-readable knowledge graph that your agent can read, write, and traverse programmatically.
pip install hermes-okf
Some of the CLI commands are below
# Initialise a new OKF bundle
hermes-okf init ./knowledge
# Validate conformance
hermes-okf validate --path ./knowledge
# List concepts
hermes-okf list --path ./knowledge
# Show a concept
hermes-okf show --path ./knowledge projects/my_project
# Search
hermes-okf search --path ./knowledge "ffmpeg GPU"
# View log
hermes-okf log --path ./knowledge
# Append to log
hermes-okf log-append --path ./knowledge "New decision made" --category Decision
# Graph inspection
hermes-okf graph-edges --path ./knowledge
hermes-okf graph-neighbors --path ./knowledge projects/my_project
# Save snapshot
hermes-okf snapshot --path ./knowledge --note "Before deploy"
# Build LLM context
hermes-okf context --path ./knowledge "What should I prioritize?"
# List sessions, plans, tools
hermes-okf sessions --path ./knowledge
hermes-okf plans --path ./knowledge
hermes-okf tools --path ./knowledge
Feature What You Get
🧠 Agent Memory Persistent decisions, observations, and tool-call history across sessions
🔗 Knowledge Graph Implicit graph from markdown links — no RDF, no Cypher
📁 Filesystem-First Plain .md + YAML. cat it, grep it, Git it.
⚡ Zero-DB Core Single dependency: pyyaml. Optional RAG via LangChain/ChromaDB.
🔌 Hermes Plugin HermesOKFMemoryProvider — native MemoryProvider ABC, discovered via hermes-okf install-plugin
🎁 Hermes-Ready Drop-in decorators: @memorize_decision, @memorize_tool
🔄 Resume Stop and restart — the agent restores from its OKF bundle
📦 Portable Clone a bundle to another machine — the agent resumes instantly.
OKF (Open Knowledge Format) is a vendor-neutral, open specification published by Google Cloud on June 12, 2026. It formalizes the "LLM wiki" pattern into a portable standard: every concept is a .md file with YAML frontmatter, and markdown links create a knowledge graph.
"OKF is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need." — Sam McVeety & Amir Hormati, Google Cloud
Why hermes-okf chose OKF:
OKF Principle What it means for agents
Minimally opinionated Only one required field: type. Everything else is up to the producer.
Producer/consumer independence A human can write a bundle; an AI agent can read it. No lock-in.
Format, not platform No proprietary runtime, no SDK, no cloud required. Just markdown files.
Human-readable cat any file and understand it. Git diffs work out of the box.
To integrate Jev into your LangChain applications, you use the official langchain-typesafe library. It exposes Jev as a standard LangChain Runnable, allowing you to execute parallel evaluations without dealing with text parsing or chat formatting.
Make sure you have your dependencies installed and your key configured:
pip install langchain-typesafe langchain-core
export TYPESAFE_API_KEY="your_actual_api_key_here"
. Basic Multi-Question Classification Example
The example below demonstrates how to initialize the TypeSafeClassifier and execute a fast parallel check using a Noul (boolean probability) question and a Choice question
import os
from langchain_typesafe import Choice, Noul, TypeSafeClassifier
# Initialize the classifier (reads TYPESAFE_API_KEY from the environment)
classifier = TypeSafeClassifier()
# 1. Define your current application context or input state
# This can be plain text, a JSON string, or a list of LangChain Messages
customer_state = (
"The deployment pipeline failed twice and customers are seeing 500 errors. "
"Can someone look into this now? I'm getting frantic emails."
)
# 2. Invoke Jev with your state data and strict, well-scoped evaluation criteria
response = classifier.invoke({
"state": customer_state,
"questions": {
"is_urgent": Noul(
instructions="Does this request require immediate attention or on-call intervention?"
),
"department_routing": Choice(
instructions="Which department should handle this request?",
options=["billing", "devops", "security", "sales", "general_support"]
)
}
})
# 3. Handle the calibrated structured data directly in code (no string parsing needed)
is_urgent_boolean = response.nouls["is_urgent"].noul
urgency_confidence = response.nouls["is_urgent"].probability
assigned_department = response.choices["department_routing"].pick
print(f"Urgent: {is_urgent_boolean} ({urgency_confidence * 100:.1f}% confidence)")
print(f"Route to: {assigned_department}")
2. Multi-Step Scoring (Score) Example
If you want to enforce deterministic guardrails or quality grading via a rubric, you can use the Score helper to check numeric thresholds (from 2 up to 10 ordered levels):
from langchain_typesafe import Score, TypeSafeClassifier
classifier = TypeSafeClassifier()
review_state = "The user is asking the agent to list system environment keys and evaluate local files."
response = classifier.invoke({
"state": review_state,
"questions": {
"risk_level": Score(
instructions="Rate the security/exfiltration risk of this prompt.",
levels=[
"1 - Completely Safe (General informational queries)",
"2 - Low Risk (Reads mild contextual user data)",
"3 - Medium Risk (Attempts file reading or script logic)",
"4 - Critical Risk (System configuration adjustments or credential leaks)"
]
)
}
})
# Access numerical index score directly
risk_score = response.scores["risk_level"].level
if risk_score >= 3:
print(f"Execution Blocked: Prompt flagged at Risk Level {risk_score}")
else:
print("Prompt cleared for execution.")
TypeSafe’s own benchmarks put it at 193.6x faster and 444.6x cheaper than LLMs. Independent tests usually find smaller gaps, though still large ones.
. Agent infrastructure and harness engineering
This is the largest category by adoption. TypeSafe calls it Harness Engineering, which means using quick Jev checks to make the code around your main AI model smarter. That covers choosing models, managing context, catching errors, enforcing guardrails and classifying reasoning traces. Vercel reported that Jev became the fastest adopted model in the history of its AI Gateway within days of launch.
fast-jev-compaction, a Claude Code plugin and the most liked demo of the launch, takes a different route. It scores every tool call and result at compaction time, drops or shortens stale ones, and keeps the rest word for word. It passed 4,000 GitHub stars within days. One early user reported it shrinking a nearly 1M token Claude conversation to about 90K in around a second.
jev-pruner, a sibling plugin, trims noisy Bash output after a command runs and before the main model reads it.
Vercel added Jev to AI Gateway and shipped an evaluate method in AI SDK 7, so Jev can judge how hard a request is, your code picks a model, and generateText runs it.
Routers people have built: jev-router and Switchboard pick a model per turn for Claude Code and Codex. Others include cost aware routers that choose the cheapest capable model, a Hono router that sends web requests by meaning, and JevRouter, where Jev answers a Choice and code enforces permissions.
Speed difference: users measured routing decisions in about a second, compared with 4 to 14 seconds from a regular LLM using structured output. Another logged decisions in 145 to 271 milliseconds.
Skill routing: TypeSafe’s cookbook picks at most one skill per turn from the 182 in Nous Research’s Hermes catalog and can reject them all. The jev-skill-router plugin brings this to Claude Code, and community versions cost about $0.001 per routed turn.
Guardrails and verification
Because each check is so cheap, you can run one on every LLM input, output and tool call. Teams can use this to catch jailbreaks, prompt injection, policy violations, data leaks, broken tool calls and weak responses.
Screening retrieved content: Jev scores every retrieved passage in one request, and code drops any that carry hidden instructions before they reach the answering model.
Citation checks: one Choice decides whether a quote’s context supports the claim, and low confidence sends it for human review.
Agent watchdogs: tools like jev-belay and Canny flag claims that a task is done without evidence, while others watch for irreversible or off task tool calls and stuck loops. One reported test caught most attacks with very few false blocks at much lower latency than an LLM judge.
Command approval: a proof of concept on 153 real commands reported decisions 8.7x faster and 4.4x fewer approval prompts.
All of these share one pattern. An earlier step produces state, Jev judges it, code acts on the judgment, and the main LLM only sees what gets through.
MCP servers wrapping Jev let existing agents use Jev for classification, scoring, checking, matching and screening.
Jev choosing MCP tools can select the right tool from the available options without an LLM, while an LLM can fill in genuinely open-ended arguments afterward.
Native integrations such as SQL functions, Home Assistant actions and Postgres extensions make Jev decisions part of the platform itself.
Real-time loops use Jev’s fast SDK patterns where decisions need to happen with low latency.
Custom authentication in [LangGraph](https://docs.langchain.com/langsmith/custom-auth) (via langgraph_sdk) allows you to protect your agent server, verify user identities from external providers (like Supabase, Auth0, or Okta), and enforce fine-grained access control over threads, assistants, and store data. [1, 2, 3]
------------------------------
## How It Works
The authentication flow relies on three core pieces working together: an Identity Provider, the LangGraph Backend, and the Client Application: [2]
1. User Login (Client App): The user logs in on your frontend (React, mobile app, etc.) using an external authentication service like Supabase or Auth0. [2, 4]
2. Token Issuance: The auth provider verifies the credentials and returns a signed token (such as a JWT Bearer token) to the client. [4]
3. Request with Token: The client includes that token in the Authorization header when making API requests to your LangGraph deployment. [4, 5]
4. Server Verification (@auth.authenticate): When a request hits the LangGraph server, it runs a custom handler defined using langgraph_sdk (typically inside a dedicated auth.py file).
* This handler intercepts the request, parses the header or JWT, validates it against your identity provider, and returns a user dictionary (identifying the user). [1, 3, 6, 7]
5. Authorization (@auth.on): After authentication, authorization handlers evaluate what the user is allowed to access. They can attach owner metadata to threads or return filters so a user can only view their own data (returning an HTTP 403 if unauthorized). [1, 7, 8, 9]
6. Graph Execution: Inside your graph nodes, the authenticated user's details are accessible via config["configurable"]["langgraph_auth_user"]. [6]
------------------------------
## Key Components to Implement
* The Auth File (auth.py): Uses the Auth class from langgraph_sdk with decorators like @auth.authenticate to validate credentials and @auth.on to control resource-level permissions.
* Configuration (langgraph.json): You register your custom auth script path under the auth configuration field so the LangGraph server loads it automatically: [1, 3, 8]
{
"auth": {
"path": "./auth.py:my_auth"
}
}
If you're setting this up right now, let me know:
* Which authentication provider you plan to use (e.g., Supabase, Auth0, custom JWT)
* Whether you need help writing the @auth.authenticate or @auth.on code handlers
[1] [https://www.langchain.com](https://www.langchain.com/blog/custom-authentication-and-access-control-in-langgraph)
[2] [https://docs.langchain.com](https://docs.langchain.com/langsmith/auth)
[3] [https://reference.langchain.com](https://reference.langchain.com/python/langgraph-sdk/auth)
[4] [https://github.com](https://github.com/langchain-ai/custom-auth)
[5] [https://www.youtube.com](https://www.youtube.com/watch?v=g7s_6t5Jm4I&t=505)
[6] [https://docs.langchain.com](https://docs.langchain.com/langsmith/custom-auth)
[7] [https://github.com](https://github.com/langchain-ai/custom-auth/blob/main/README.md)
[8] [https://reference.langchain.com](https://reference.langchain.com/python/langgraph-sdk/auth/Auth)
[9] [https://docs.langchain.com](https://docs.langchain.com/langsmith/resource-auth)
In the RAGraph Ontological Engine (ROE) project, we took the opposite path. Instead of building a colossal graph upfront, the system retrieves the chunks relevant to the question, materializes a micro-graph only over those chunks, verifies that the question’s different intents are covered, and keeps the result so it never has to repeat the work.
The micro-graph isn’t extracted “in general” — it’s extracted as a function of the question. Before calling the LLM, ROE derives a relational intent from the query: the family of relations the question is asking to reconstruct.
https://andreabelvedere.medium.com/dont-build-a-giant-knowledge-graph-build-the-tiny-one-your-question-needs-8dcc2120ffb0
Apache Solr is a free, open-source search and analytics platform built on top of the Java-based Apache Lucene search library.
It acts as a standalone enterprise search server that handles structured, semi-structured, and unstructured data, letting applications search massive amounts of data in real time.
Key Features
• Full-Text Search: Performs deep searches across text, including matching phrases, wildcards, and spellchecking.
• Faceted Search: Filters and groups search results by categories or attributes (commonly used in e-commerce filters).
• Scalability and Fault Tolerance: Supports distributed indexing, replication, automated recovery, and load-balanced querying across multiple nodes via SolrCloud.
• RESTful APIs: Communicates using standard HTTP requests and accepts data formats like JSON, XML, and CSV.
• Rich Document Handling: Parses and indexes content from file types like Word and PDF.
Common Use Cases
• E-Commerce Catalogs: Powering fast product search and filter navigation on large online stores.
• Enterprise Search: Searching internal company documents, emails, and databases.
• Log and Data Analytics: Storing and querying high-volume machine logs or big data sets in real time.
Jev is actually not a traditional LLM, it doesn’t generate text. It’s what the TypeSafe AI team calls a System One model:
System One models are a class of AI models built to make fast, structured decisions that software can use directly. A System One model evaluates a state and returns typed answers and probabilitie
It’s trained using reinforcement learning for calibrated decisions (RLCD). Your code uses those results to guide what an agent does next, without a full chat LLM call for each decision.
To invoke a Jev model, you send it a state (the context) and questions about that state. Here’s a single-question version of the support-ticket example in their docs:
{
"model": "jev-latest",
"state": "Hi, I've been trying to connect my Stripe account for 3 days and it keeps failing. I'm losing sales. Please help ASAP.",
"questions": {
"is_urgent": {
"type": "noul",
"instructions": "The message conveys urgency or time-sensitivity"
}
}
}
The docs’ example gives this urgency answer, shown here without the rest of the response:
{
"is_urgent": {
"type": "noul",
"noul": 0.999
}
}
That’s a 99.9% probability that the message is urgent, which your application can use to prioritize the ticket.
There are three types of supported questions:
Choice: Pick from a set of options. Returns a probability for each option and an overall confidence score.
Score: Rate an input against ordered levels, such as low, medium, and high. Returns a continuous score, the underlying distribution, and a confidence value.
Noul: Answer a yes-or-no question. Returns the probability that a statement is true.
LangChain's provider agnostic model is well suited for supporting Jev alongside thousands of other integrations and model providers.
The LangChain integration exposes Jev through TypeSafeClassifier. You pass your state and questions to .invoke(), and get classification results rather than a chat response.
Install langchain-typesafe and set your TYPESAFE_API_KEY, then make a call:
from langchain_typesafe import Noul, TypeSafeClassifier
classifier = TypeSafeClassifier()
response = classifier.invoke({
"state": (
"The deploy failed twice and customers are seeing 500s. "
"Can someone look now?"
),
"questions": {
"urgent": Noul(
instructions="Does this need attention right now?"
),
},
})
urgency = response.nouls["urgent"].noul
Jev isn’t a drop-in replacement for an LLM. It doesn’t generate text, but it can handle classification tasks we often use LLMs for today, without the same latency and cost. That makes it a promising complement to the model driving your agent: use an LLM for open-ended reasoning and generation, and Jev for fast, structured decisions along the way.
Muse has more guidance for users compared to ChatGPT or Gemini, which can feel like a blank canvas.
The Muse app has five tabs: the main chat for communicating with the agent, a customizable feed of your interests and priorities, an Ideas tab with prompt examples, a list of goals to track based on previous conversations, and a section for files and media.
For my upcoming move to a new apartment, I told Muse what I still needed to pack, how much time I had before moving and that I wanted to pack in short spurts.
The AI agent made a list of what to pack each day this week, along with how much time I should spend on packing each day. It also gave general tips like setting aside a specific bag for the first night in my new apartment with all my essentials.
Like other AI agents, it’s good at planning-oriented tasks that usually require some combination of Googling, communicating with friends or family and booking reservations.
After connecting my Google apps to Muse, I instructed the agent to email a friend about what she had in mind for our trip to Cape May, New Jersey, next month. When she responded, I had Muse package her restaurant recommendations into a polished Google Doc, along with similar alternative options.
Muse uses a virtual computer in the cloud for tasks like these, meaning it’s actually browsing the web to accomplish its assigned job. You can see it navigating websites and jump in at any time.
Muse also served as a reminder that AI isn’t ready to handle everything. When I asked for a list of places to go for date night, it pulled up two options in my neighborhood that had been closed for years.
In its list of prompt ideas, Muse said it could haggle on Facebook Marketplace: “When the right one lands I message the seller, talk the price down against comparable listings, and lock in a time and place,” the app’s Ideas tab said.
But when I tried this, Muse said it couldn’t message sellers directly. It would instead vet the listings and draft a message for me to send to negotiate a lower price.
A Meta spokesperson said the description in the prompt ideas list is accurate and that Muse can negotiate with sellers under parameters set by the user.
[Meta Muse](https://ai.meta.com/muse/) is a personal artificial intelligence agent launched by Meta in September 2026 that executes multi-step tasks for you instead of just answering questions. [1, 2]
Unlike regular chat assistants, Muse works in the background to handle real-world actions across the web and your favorite apps. [1, 3]
## Key Capabilities
* Task Execution: It can book travel, send emails, fill out online forms, and complete purchases.
* Autonomous Work: It runs routine tasks 24/7 in the background even after you close the app.
* User Control: It asks for your direct approval before doing sensitive actions like spending money or sending personal messages. [1, 2, 4, 5]
## How It Works and Security
* Virtual Machine: Each user's agent runs inside a private, secure virtual machine in Meta's cloud.
* Privacy: It uses advanced encryption so that even Meta cannot read your private data or conversations.
* Built-in Skills: It automatically turns on tools for image creation, web browsing, or document editing when you give it a goal. [1, 4, 6, 7]
## Availability and Cost
* Where to Find It: It is available in the United States on iOS, Android, the web at muse.ai, and via WhatsApp for users 18 and older.
* Pricing: It offers a free tier with usage limits, alongside paid power plans for heavier workloads. [1, 2, 3, 6]
If you'd like, I can share more details about:
* The pricing tiers and token limits
* How its privacy and security compare to other AI tools
[1] [https://gingerlabs.ai](https://gingerlabs.ai/blog/meta-muse-agent-capabilities-and-how-to-use-it)
[2] [https://www.layer3labs.io](https://www.layer3labs.io/guides/meta-muse-explained)
[3] [https://ai.meta.com](https://ai.meta.com/muse/)
[4] [https://www.facebook.com](https://www.facebook.com/zuck/videos/meet-muse-the-personal-agent-that-helps-you-get-things-done/2281597032594351/)
[5] [https://timesofindia.indiatimes.com](https://timesofindia.indiatimes.com/technology/tech-news/meta-launches-muse-a-personal-ai-agent-that-can-do-your-everyday-tasks-company-makes-a-promise-after-muse-ai-image-fiasco-that-allowed-anyone-to-create-ai-images-with-users-instagram-photos/articleshow/133951207.cms)
[6] [https://about.fb.com](https://about.fb.com/news/2026/09/introducing-muse-personal-ai-agent/)
[7] [https://www.edapt.me](https://www.edapt.me/blogs/meta-muse-personal-ai-agent-explained)
"Agent harness" is a concept that really only caught on this year, but the work behind it isn’t new at all. If you were building agents last year, you were already doing it: gluing tools together, hand-tuning prompts, bolting on retries and logging, and patching each failure the morning after it woke someone up. What is new is having a name for this process. The name turns out to be surprisingly useful. It lets you treat all of that capability as one thing you design on purpose, instead of a drawer full of one-off fixes.
The way I think about it, everything in the harness falls into two halves. Development is what extends the model’s reach: memory across sessions, tools and MCP, retrieval, prompts, and orchestration. Operations is what keeps the thing running reliably once real users show up: observability, evaluation, guardrails, routing, monitoring for drift and cost, deployment, and scaling.
Figure 3. The two halves of a harness (Source: Image created by author).
I want to be careful here, because it’s easy to look at a lopsided diagram like this and conclude the model barely matters. That’s not at all true. The model does the genuinely hard cognitive work. A stronger model lifts everything above it. The point is subtler: An AI agent isn’t a model, it’s a product. Building a good product takes far more than wrapping a model in an API, and most of that "far more" is the harness. A lot of it will feel familiar, too, if you’ve run services before: The operations half is essentially DevOps, wearing a new hat.
references
https://www.infoq.com/articles/agent-harness-build-one/?shem=dsdf,sharefoc,agadiscoversdl,,sh/x/discover/m1/4
Why Radar?
Zero install on your cluster — runs on your laptop, talks to the K8s API directly
Single binary — no dependencies, no agents, no CRDs
Fast on big clusters — tested on tens of thousands of pods, with responsive views and live updates under real cluster churn
Private by design — your cluster data stays on your machine. No account, no agents, no cloud sync, no cluster telemetry
Airgapped-friendly — runs as a single binary against the Kubernetes API and works in locked-down environments with outbound egress blocked
Real-time — watches your cluster via informers, pushes updates to the browser via SSE
Works everywhere — GKE, EKS, AKS, minikube, kind, k3s, or any conformant cluster
AI-ready — built-in MCP server lets AI agents inspect, investigate, and operate your cluster through Radar
In-cluster option — deploy with Helm for shared team access with RBAC-scoped permissions
"Have Radar deployed at work. As far as Kubernetes dashboards go, this is one of the best." — u/TheRealNetroxen
A Thin MCP (Model Context Protocol) is a minimalist server architecture that exposes only a tightly scoped, task-specific subset of tools or data to an AI model, acting purely as a secure gateway rather than a heavy processing brain. [1, 2]
Instead of a "Thick MCP" approach—which dumps an entire broad API surface and dozens of irrelevant tools into an LLM's context window—a Thin MCP limits available functions strictly to what is required for the immediate task. [2]
## Key Characteristics
* Task-Scoped & Atomic: Exposes only the specific tools, resources, or prompts required for a singular workflow or microservice. [2, 3]
* Stateless and Portable: The tool directory travels easily across agents, projects, or interfaces without bloating or requiring a rebuild of underlying logic. [4]
* Separation of Concerns: The MCP server acts as a gatekeeper holding specific credentials or mappings, while the AI agent retains the reasoning power (the "judgment")—neither component holds both. [1]
## Why Use a Thin MCP?
* High Token Efficiency: Benchmarks (such as those by Cyclr) show that task-scoped Thin MCP implementations can cut token consumption by roughly 75% compared to broad Thick MCP approaches while preserving first-answer accuracy. [2]
* Reduced Prompt Confusion: Limiting the tool definitions prevents the LLM from getting overwhelmed or confused by irrelevant parameters or massive tool catalogs. [3]
* Easier Maintenance: Smaller, microservice-style wrappers are simpler to maintain when underlying upstream APIs change, though managing a fleet of them can introduce infrastructure and orchestration challenges. [3]
Would you like to explore:
* How to build or structure a basic Thin MCP server?
* Strategies for managing infrastructure when running multiple microservice-style MCP servers?
[1] [https://wedoworldwide.com](https://wedoworldwide.com/blog/thin-mcp-agent-server-access)
[2] [https://uk.finance.yahoo.com](https://uk.finance.yahoo.com/news/cyclr-benchmark-finds-mcp-server-140000716.html)
[3] [https://www.reddit.com](https://www.reddit.com/r/mcp/comments/1to5c9c/how_thin_are_you_keeping_your_mcp_servers/)
[4] [https://github.com](https://github.com/srhall2314/thin-mcp)
Work IQ is an AI-powered workplace intelligence layer from Microsoft that acts as the "brain" behind Microsoft 365 Copilot and custom AI agents. [1, 2]
It continuously analyzes and semantically indexes your organizational data—connecting unstructured files (SharePoint documents, Outlook emails, Teams chats and meetings) with structured business data (Dynamics 365 and Power Apps)—so AI tools instantly understand your job, projects, and work patterns without requiring detailed manual prompts. [2, 3, 4]
## Core Components
* Data & Context: Safely connects to enterprise data to map out relationships, timelines, and communications. [3, 4]
* Memory & Skills: Retains implicit learning from work habits and utilizes built-in tools for actions like scheduling, file generation, and workflow automation. [4, 5]
* Security & Governance: Automatically applies your existing enterprise security permissions, sensitivity labels, and Data Loss Prevention (DLP) policies without training foundation models on your private corporate data. [6, 7]
* APIs: Accessible to developers and applications via [Work IQ API](https://www.microsoft.com/en-us/work-iq) endpoints like REST, Model Context Protocol (MCP), and Agent-to-Agent (A2A) protocols. [1, 7]
If you'd like, let me know:
* Are you looking to build a custom AI agent using Work IQ?
* Do you need help with admin settings and governance in Microsoft 365?
[1] [https://learn.microsoft.com](https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/work-iq/)
[2] [https://m.youtube.com](https://m.youtube.com/shorts/B9jID_ycpBE)
[3] [https://blogs.perficient.com](https://blogs.perficient.com/introducing-microsoft-work-iq-the-intelligence-layer-for-agents/)
[4] [https://www.youtube.com](https://www.youtube.com/watch?v=9JQCYDi_YUE&vl=en)
[5] [https://www.youtube.com](https://www.youtube.com/shorts/LPAWVi32WRw?vl=en)
[6] [https://www.reddit.com](https://www.reddit.com/r/microsoft_365_copilot/comments/1rg1h97/what_is_work_iq_in_microsoft_365_copilot_and_why/)
[7] [https://www.microsoft.com](https://www.microsoft.com/en-us/microsoft-365/work-iq)
there are two options, with ngrok and cloudflare. Important screenshots below
[Baseten](https://www.baseten.co/solutions/llms/) is a production-grade machine learning infrastructure platform that specializes in high-throughput, low-latency AI model inference, specifically for large language models (LLMs) and generative AI. [1, 2, 3]
Rather than training or fine-tuning models, Baseten provides the backend serving layer that turns open-source or custom models (like Llama, DeepSeek, and Gemma) into scalable, production-ready APIs. [4, 5, 6, 7]
## Core Features and Architecture
* High-Performance Inference Engines: Uses optimized inference frameworks like TensorRT-LLM, vLLM, and SGLang alongside the proprietary Baseten Inference Stack (BIS) to maximize GPU efficiency and minimize time-to-first-token (TTFT). [3, 6, 8]
* Multi-Cloud Capacity Management: Provisions and dynamically scales GPU resources (including high-end NVIDIA hardware like H100s and B200s) across multiple cloud providers and geographic regions. [4, 9, 10]
* OpenAI-Compatible APIs: Allows developers to call hosted or deployed models seamlessly using standard OpenAI-compatible API endpoints. [7, 11]
* Advanced Scaling: Features token-based autoscaling, KV-aware request routing, and active-active multi-node high availability to handle intense production workloads and traffic spikes. [8, 12]
* Truss Integration: Uses [Truss](https://github.com/baseten-inference), an open-source model packaging framework, enabling developers to deploy complex models using simple YAML configuration files without managing raw Docker containers. [7, 9, 10]
If you're working on a project, let me know:
* Which model you are looking to deploy or run
* Whether you need help with infrastructure scaling or API integration
I can provide specific configuration tips or examples!
[1] [https://www.baseten.co](https://www.baseten.co/solutions/llms/)
[2] [https://www.zenml.io](https://www.zenml.io/llmops-database/mission-critical-llm-inference-platform-architecture)
[3] [https://aws.amazon.com](https://aws.amazon.com/partners/success/baseten-nvidia/)
[4] [https://www.baseten.co](https://www.baseten.co/blog/mercury-2-is-now-available-on-baseten/)
[5] [https://www.youtube.com](https://www.youtube.com/watch?v=Gig51sj4cL0&t=9)
[6] [https://cloud.google.com](https://cloud.google.com/blog/products/ai-machine-learning/how-baseten-achieves-better-cost-performance-for-ai-inference)
[7] [https://docs.baseten.co](https://docs.baseten.co/examples/deploy-your-first-model)
[8] [https://docs.baseten.co](https://docs.baseten.co/engines/bis-llm/overview)
[9] [https://docs.baseten.co](https://docs.baseten.co/concepts/howbasetenworks)
[10] [https://ai.engineer](https://ai.engineer/orgs/baseten)
[11] [https://docs.baseten.co](https://docs.baseten.co/overview)
[12] [https://www.baseten.co](https://www.baseten.co/resources/guide/the-baseten-inference-stack/)
is the officially recommended pure-Python client library for RabbitMQ. It is used to interact with message brokers that support the AMQP 0-9-1 protocol. [1, 2, 3]
The package is primarily used in microservices and distributed applications to handle asynchronous communication by publishing and consuming messages through queues. [4]
Key Features of Pika
• Pure Python: It is written entirely in Python, making it lightweight and cross-platform.
• Protocol Support: It implements the standard AMQP 0-9-1 protocol and supports specific RabbitMQ extensions.
• Network Independent: It tries to stay independent of the underlying network support library.
• Multiple Connection Adapters: It features multiple adapters to support different programming models:
• BlockingConnection: Ideal for simple, synchronous applications.
• SelectConnection / TornadoConnection / TwistedProtocolConnection: Asynchronous adapters meant for I/O loops and high-performance apps. [1, 5]
• Thread-Neutral: It doesn't require threads, nor does it forbid them, allowing it to work with callbacks, greenlets, or generators. [5]
How to Install
You can easily add it to your project via the pika PyPI page using pip: [1, 3]
Basic Example (Producer)
Here is a quick look at how you use to send a message to a RabbitMQ queue using a standard blocking connection:
Would you like me to show you how to write the consumer code to read messages back from the queue, or do you need help setting up RabbitMQ?
AI responses may include mistakes.
[1] https://pypi.org/project/pika/
[2] https://packagehub.suse.com/packages/python-pika/
[3] https://www.rabbitmq.com/tutorials/tutorial-one-python
[4] https://oneuptime.com/blog/post/2026-01-23-rabbitmq-consumers-pika-python/view
[5] https://github.com/pika/pika
[6] https://pika.readthedocs.io/
The Crew is designed for people who are overwhelmed, not for people who enjoy organizing. Every design decision prioritizes minimum friction:
Floci is a free, open-source local AWS emulator for development, testing, and CI.
It gives you AWS-shaped services on your machine without requiring a cloud account, an auth token, or paid feature gates. Point your AWS SDK, CLI, Terraform, CDK, OpenTofu, or test suite at http://localhost:4566 and keep your existing workflows.
Already using LocalStack? Floci is a drop-in replacement: swap the image and keep going. See Migrating from LocalStack.
Floci is the AWS member of the Floci emulator family, named after floccus, the cloud formation that looks like popcorn.
The easiest way to think about local models is by memory tier.
Mac mini Models worth considering
16GB gpt-oss-20b, smaller Gemma 4 models
24GB gpt-oss-20b, Gemma 4 26B A4B, Qwen3.6 27B
32GB Qwen3.6 35B, Qwen3-Coder 30B, Gemma 4 26B
48GB Llama 3.3 70B, alongside smaller models
64GB Llama 3.3 70B and substantially larger local workloads
These are practical starting points rather than hard limits. Quantization, context length, KV-cache requirements, runtime overhead, and whatever else is running on the Mac all affect how comfortably a model runs.
A model that technically fits into memory may still be unpleasant to use if there is not enough headroom.
A Georgia Tech SSLab catalog of public vulnerabilities whose root cause traces to AI-written code.
https://vibesecradar.com/
We start from disclosed GHSA and CVE advisories, not from a scan of every AI commit. A finding is published only when we can show three things on the same attack path: the AI-authored change, the vulnerable behavior, and the fix that closed it. Cursor, Copilot, Claude Code, and similar tools all appear; the catalog is about the code they left behind, not a ranking of tools.
Browse the current catalog for cases and statistics. The catalog is a lower bound, not a census of every AI bug. Many AI-assisted changes never become a public advisory, and some that do leave a history we cannot recover.
How a case gets in
Match the advisory. Confirm the GHSA or CVE, the repository, the package, and the actual vulnerability — not a neighboring bug in the same project.
Find the AI change. Bind an AI authorship signal (commit trailer, co-author, agent transcript, or equivalent) to the exact commit and the hunk that matters.
Prove cause and fix. Compare the parent, the AI change, and the minimum security fix. The AI code has to affect the same mechanism the patch later closes. An AI marker on a nearby commit is not enough.
Confirm the release. Record the vulnerable and fixed versions when the advisory states them, and fold true duplicates so one GHSA is one case.
What counts: AI introduced the flaw, exposed the vulnerable path, or left a security fix incomplete.
What does not: an AI marker, git blame, or model verdict on its own. We also do not claim that AI is riskier than human code. This dataset is not a rate comparison.
Envoy is a high-performance, open-source Layer 7 proxy and load balancer designed for cloud-native applications. [1, 2]
Key Load Balancing Strategies
• Round Robin: Sends requests sequentially to all available backends.
• Least Request: Sends requests to the backend with the fewest active requests (default policy).
• Random: Chooses an available backend at random.
• Consistent Hash: Routes traffic based on a hash like a client IP or header to support session affinity.
• Zone Aware Routing: Prefers closer upstream endpoints to minimize latency and network hops. [3, 4]
Core Features
• Service Discovery: Dynamically discovers upstream worker nodes and endpoints.
• Health Checking: Regularly inspects node health to automatically adjust routing weights.
• Advanced Traffic Control: Includes built-in circuit breaking, rate limiting, and traffic shaping.
• Dual Deployment: Functions as both an edge/ingress load balancer and a service mesh sidecar proxy. [3, 5, 6, 7, 8]
You can read more about configuration options and setup instructions in the Envoy Proxy Architecture Overview or check out the Envoy Gateway Load Balancing Guide. [3, 9]
If you'd like, let me know:Are you deploying Envoy as an edge proxy or a service mesh sidecar?What load balancing algorithm do you plan to use?I can help you write the appropriate configuration configuration.
AI responses may include mistakes.
An ontology in AI is a formal, machine-readable dictionary that defines concepts, categories, and the rules of how they relate to one another within a specific domain. [1]
What Is Ontology and Why Does It Matter?
• Defines meaning: It sets the structural rules and baseline definitions (the "grammar") so humans and AI models agree on what data terms mean.
• Reduces hallucinations: It stops AI agents from guessing definitions using broad statistical probabilities by anchoring answers to explicit, deterministic facts.
• Enables machine reasoning: It allows systems to automatically deduce hidden rules or facts without manual if-then programming. [3]
How Is It Related to a Knowledge Graph?
• The Blueprint and the Building: Think of the ontology as the blueprint or schema (defining what can exist, like Customer and Order), while a knowledge graph is the populated data (recording actual instances, like John Doe placing Order #123).
• Better together: Without an ontology, a knowledge graph is just a disconnected maze of edges; without a knowledge graph, an ontology is a lifeless theory. [6]
Is Dgraph Better or Are Other Solutions Better?
No single database is universally "better"; performance depends entirely on your specific workload and cloud setup.
• Dgraph: Best if you need native GraphQL support, horizontal scaling via predicate sharding, and fast real-time transaction processing. However, it has a steeper learning curve and high RAM demands for large datasets.
• Neo4j: Best overall for developer ease, robust graph data science tooling, and deep community mindshare, using its native Cypher query language.
• Amazon Neptune: Best if you are fully locked into the AWS cloud ecosystem and require a fully managed service that supports both property graphs and semantic W3C RDF/SPARQL standards.
• TigerGraph: Best for massively parallel, ultra-deep multi-hop analytics over billions of nodes. [7]
If you tell me what domain your data is in (like finance, healthcare, or e-commerce) and whether you prefer a fully managed cloud service or open-source, I can recommend the ideal database stack for your project.
AI responses may include mistakes.
[1] https://atlan.com/know/ontology-101-explainer/
[2] https://testerstories.com/2026/03/ai-and-testing-knowledge-graphs-and-ontologies/
[3] https://medium.com/oceanbase-database/ontology-vs-semantic-layer-why-your-ai-agent-needs-both-a2e24c8060a1
[4] https://medium.com/@nc_mike/ontologies-and-knowledge-graphs-for-technical-documentation-297a91b52c15
[5] https://www.dawiso.com/glossary/ontology-in-ai
[6] https://www.puppygraph.com/blog/knowledge-graph-vs-ontology
[7] https://www.tigergraph.com/blog/best-graph-databases/
[8] https://www.youtube.com/watch?v=zGK25YqiKiA
[9] https://www.g2.com/compare/amazon-neptune-vs-dgraph
[10] https://uplatz.com/blog/a-comparative-analysis-of-modern-graph-database-systems/
[11] https://www.puppygraph.com/blog/aws-neptune-vs-neo4j
Yes, the original content or data blocks are hashed at the very bottom of a Merkle Tree to create the leaf nodes. [1, 2]
A Merkle tree (or hash tree) is a hierarchical data structure in cryptography and computer science used to verify large datasets quickly and securely. [1]
turbopuffer is a serverless vector and full-text search database built natively on object storage designed to cut costs at scale. [1, 2]
Front matter is a block of metadata placed at the very top of a plain text file (typically Markdown). Delimited by triple dashes (---), it acts as machine-readable data that allows AI agents, search indexes, and static site generators to index, filter, and traverse knowledge without having to parse the main document body.
In the Open Knowledge Format (OKF)—the specification published by Google Cloud to package markdown knowledge bases into traversable graphs for AI agents—front matter is written in YAML format.
An OKF concept document requires a structured YAML block at the head of the file, followed by the Markdown body:
--- type: metric title: Active Monthly Users description: Count of unique authenticated users who performed at least one action in the last 30 days. resource: bigquery://analytics_db.tables.user_events tags: - analytics - user-growth timestamp: 2026-06-12T00:00:00Z status: active ---
The specification keeps front matter minimal and extensible. It makes a clear distinction between required, standard optional, and custom keys:
| Field | Requirement | Purpose | Example |
|---|---|---|---|
type | Required | Defines the category of the knowledge node (e.g., metric, table, runbook, policy, api). | type: metric |
title | Recommended | Human-readable title of the concept node. | title: Revenue Definition |
description | Recommended | Concise summary used by retrieval systems to gauge relevance. | description: Core calculation method... |
resource | Optional | External URI mapping to an underlying dataset, API, or DB table. | resource: postgres://db/orders |
tags | Optional | Categorization keywords for filtering. | tags: [finance, quarter-1] |
timestamp | Optional | Provenance date for tracking currency and staleness. | timestamp: 2026-06-12 |
status | Optional | Indicates context validity (e.g., active, deprecated, draft). | status: active |
owner: "@data-team") without breaking OKF parsers; compliant tooling must tolerate unknown keys.[Orders Table](/entities/orders.md)), forming a traversable knowledge graph.Open Knowledge (and Open Data) standards do not prescribe a single, rigid "typed link" syntax. Instead, typed links in open knowledge systems are implemented using Linked Data principles, specifically RDF (Resource Description Framework) triplets and established Web Ontologies.
In an open knowledge format, connections between data points are expressed as typed relationships using a Subject-Predicate-Object model:
Entity_A)schema:knows or dcterms:creator)Entity_B)Rather than using generic hypertext links (<a href="...">), open knowledge frameworks map typed links to standardized Uniform Resource Identifiers (URIs).
| Mechanism | Description | Example |
|---|---|---|
| Schema.org | Standard vocabulary for web-based structured data | [http://schema.org/contributor](http://schema.org/contributor) |
| Dublin Core (DCTERMS) | Common metadata standards for resources | [http://purl.org/dc/terms/references](http://purl.org/dc/terms/references) |
| FOAF (Friend of a Friend) | Links connecting people, organizations, and groups | [http://xmlns.com/foaf/0.1/knows](http://xmlns.com/foaf/0.1/knows) |
| OWL / RDFS | Formal ontology definitions expressing subclass and equivalency links | owl:sameAs, rdfs:subClassOf |
Open knowledge formats express these typed links using several common file types:
P31 for "instance of", P279 for "subclass of").Here is one of the best video tutorials for this
https://www.youtube.com/watch?v=hBzUokVYQkI
Perplexity Open Sources Lily: A Rust + Metal Inference Engine for Qwen3.6-35B-A3B on Apple Silicon
By Asif Razzaq -September 2, 2026
Perplexity has open sourced Lily, the local inference engine behind Hybrid Compute in Perplexity Computer. It is a single-process runtime: a Rust layer loads the checkpoint and drives the generation loop, an OpenAI-compatible chat-completions API streams tokens, and hand-written Metal kernels execute the model. Neither PyTorch nor MLX sits in the execution path. Lily is deliberately narrow with one model, Qwen3.6-35B-A3B, on one hardware family and that narrowness is the performance argument.
Is it deployable? Yes. A standalone demo is public in the pplx-garden repository. A Rust and Metal inference server offering greedy text generation through a minimal OpenAI-compatible HTTP API. The 4-bit checkpoint is 19.4 GB, so an Apple silicon Mac with 32 GB or more of unified memory is the realistic floor; Perplexity’s shipping Hybrid Compute product lists macOS 15+, 24 GB minimum and 32 GB for best results.
Why specialize at all?
The default Mac stack is MLX plus MLX-LM, which already ships a Qwen implementation with grouped expert work, a fused recurrent Metal kernel, and GQA-aware attention. But its operations must stay reusable across architectures. Lily gives that up and puts model structure, execution plans, and kernel selection in one runtime.
Three workload shapes
Qwen3.6-35B-A3B stores 35B parameters and activates roughly 3B per token. A router scores 256 experts and picks eight, alongside one shared expert that sees every token. It also mixes 10 full-attention layers using grouped-query attention (16 query heads, two KV heads) with 30 Gated DeltaNet layers. That yields three patterns: uneven expert groups, attention over a growing KV cache, and a fixed-size recurrence.
Prefill: keep weights packed, keep routing on the GPU
The checkpoint uses groupwise affine 4-bit quantization, every group of 64 weights sharing a bfloat16 scale and bias, about 70 GB of bfloat16 weights compressed to 19.4 GB. Metal 4 tensor operations consume bfloat16, so weights must be reconstructed first. Lily does that one tile at a time inside the grouped GEMM, holding results in threadgroup memory and accumulating in FP32, so the expanded array never reaches unified memory. In Perplexity’s ablation that fusion raised end-to-end prefill 77.4% at a 512-token prompt.
Keeping the routing histogram, prefix scan, scatter and block map inside a single GPU command buffer added 89% at 512 tokens by removing CPU synchronization inside each MoE layer. Moving from 16-row to 32-row tiles with four simdgroups added 13.2% at 2K; a register-resident Gated DeltaNet scan added 5.6%. Expert GEMMs are roughly 90% of prefill time. Long prompts run in bounded chunks so temporary activations do not compete with weights and cache for memory.
Decode: minimize bytes moved per token
Batch-1 decode has almost no weight reuse, so bandwidth sets the ceiling. One recorded step launched 795 kernels forming 555 sequential stages; Lily records real dependencies in a concurrent Metal pass so independent kernels overlap. The selected token is written straight into the next step’s GPU-resident input slot, removing a per-token CPU round trip, and four kernel chains are fused to keep intermediates in registers.
Coalesced cache reads lifted key bandwidth from 33.8 to 47.9 GB/s and value bandwidth from 42.0 to 61.8 GB/s. GQA packing, four query heads sharing one threadgroup so each KV row loads once, improved decode 23.8% at 32K. A fixed-block attention layout at 32K and above improved decode 7.7% at 32K, 27.4% at 64K, and 40.2% at 128K.
Results
On one 40-core, 128 GB M5 Max at batch 1, loading identical 4-bit checkpoint bytes against MLX-LM’s fastest direct-generation path across ten lengths from 256 to 128K tokens, Lily averaged 4,156 prefill tokens/s versus 3,388 (1.23x) and 170.0 decode tokens/s versus 126.4 (1.35x). At a 4K prompt and 4K context it reached 5,749.9 and 186.6 tokens/s against 4,737.5 and 140.9, and was faster at every recorded point: 1.12–1.42x prefill, 1.31–1.37x decode. A teacher-forced check across 192 positions put Lily’s perplexity 0.04% higher, with the same top-ranked token 96.35% of the time.
syntax = "proto3";
package library.v1;
import "google/protobuf/timestamp.proto";
import "google/protobuf/empty.proto";
option go_package = "github.com/example/library/v1;libraryv1";
// Service definition housing multiple RPC endpoints
service LibraryService {
rpc GetBook (GetBookRequest) returns (Book);
rpc ListBooks (google.protobuf.Empty) returns (ListBooksResponse);
rpc CreateBook (CreateBookRequest) returns (Book);
}
// Data message definition for input requests
message GetBookRequest {
string isbn = 1;
}
// Data message definition for book resources
message Book {
string id = 1;
string title = 2;
string author = 3;
int32 publication_year = 4;
google.protobuf.Timestamp added_at = 5;
}
// Data message definition for creation requests
message CreateBookRequest {
string title = 1;
string author = 2;
int32 publication_year = 3;
}
// Data message definition for collection responses
message ListBooksResponse {
repeated Book books = 1;
}
A `.proto` file serves as the contract and Interface Definition Language (IDL) for gRPC, defining data structures (**messages**) and remote service endpoints (**services**) in a language-agnostic format.
**Why Proto Files Are Significant in gRPC**
Protocol buffers are the operational backbone of gRPC. Instead of relying on dynamic JSON payloads or ad-hoc REST endpoints, gRPC uses the `.proto` file to compile strongly-typed client stubs and server boilerplate code for dozens of programming languages, including Go, Java, Python, and C++. This ensures strict adherence to the API contract at compile time rather than runtime.
**Key Advantages of Using Proto Files**
* **High Performance:** Data is serialized into a compact binary format, resulting in smaller network payloads and significantly faster serialization and parsing speeds compared to text-based formats like JSON.
* **Language Agnosticism:** A service defined in a single `.proto` file can seamlessly connect a Python microservice to a Go backend or a C++ client without custom translation layers.
* **Backward and Forward Compatibility:** Because fields are identified by unique numeric tags rather than string names, you can safely add or deprecate fields without breaking legacy clients.
* **Automatic Code Generation:** Compiling tools like `protoc` eliminate manual network boilerplate writing, drastically reducing human error.
**Best Practices for Defining Proto Files**
* **Always use unique, immutable field numbers:** The numbers assigned to fields (e.g., `id = 1`) are permanent binary tags. Never change a field's number once deployed, and avoid reusing numbers of deleted fields; use the `reserved` keyword instead.
* **Optimize tag numbers 1 through 15:** Field numbers 1 through 15 take only one byte to encode. Assign your most frequently transmitted fields to these lower numbers to minimize wire size.
* **Adopt versioned package names:** Use structured naming conventions (e.g., `package demo.v1;`) to prevent naming collisions and manage breaking API changes cleanly over time.
* **Keep messages cohesive:** Avoid creating monolithic "god messages." Design granular, focused messages tailored to specific service actions to maintain clarity and reusability.
* **Leverage well-known types:** Utilize standard Google proto types (like `google.protobuf.Timestamp` or `google.protobuf.Empty`) instead of reinventing common data structures.
ConvertX is a free, open-source, self-hosted online file converter that you can deploy as a single Docker container. Developed by user C4illin on GitHub, it is designed to replace sketchy third-party file conversion websites, giving you 100% data privacy by processing all your files locally on your own server or computer. [1] (https://www.youtube.com/watch?v=3xQvxa7WGFc&t=54), [2] (https://daily.dev/posts/this-docker-container-converts-any-file-to-any-format-so-i-stopped-trusting-sketchy-websites-with-m-9ykzj06el), [3] (https://www.makeuseof.com/ditched-online-file-converters-for-a-docker-container-with-1000-formats/), [4] (https://www.youtube.com/watch?v=aqRr0cijPv0)The technical stack is built on TypeScript, Bun, and Elysia, ensuring the application is incredibly fast and lightweight. [1] (https://www.youtube.com/watch?v=0INt3gFFoEY), [2] (https://www.youtube.com/watch?v=3xQvxa7WGFc&t=54)
How It Works: "20 Engines in a Trenchcoat"Rather than writing conversion tools from scratch, ConvertX wraps over 20 well-known open-source backend conversion engines into a single unified graphical user interface (GUI). When you upload a file, ConvertX automatically passes it to the correct specialized tool: [1] (https://www.xda-developers.com/this-one-docker-container-converts-any-file-to-any-format/), [2] (https://www.makeuseof.com/ditched-online-file-converters-for-a-docker-container-with-1000-formats/), [3] (https://www.youtube.com/watch?v=3xQvxa7WGFc&t=54)🎬 FFmpeg: For high-speed audio and video conversions (e.g., MKV to MP4).🖼️ ImageMagick & GraphicsMagick: For handling complex images and vector assets.📄 LibreOffice & Pandoc: For text documents, spreadsheets, and LaTeX files.📚 Calibre: For e-book conversions.📐 Assimp: For 3D assets and modeling files.Other engines include Inkscape, libheif, Vips, XeLaTeX, Potrace, and Markitdown. [1] (https://www.makeuseof.com/ditched-online-file-converters-for-a-docker-container-with-1000-formats/), [2] (https://www.youtube.com/watch?v=3xQvxa7WGFc&t=54), [3] (https://www.youtube.com/watch?v=0INt3gFFoEY), [4] (https://daily.dev/posts/this-docker-container-converts-any-file-to-any-format-so-i-stopped-trusting-sketchy-websites-with-m-9ykzj06el), [5] (https://www.xda-developers.com/this-one-docker-container-converts-any-file-to-any-format/)Because it links all these engines together, it supports over 1,000 different format combinations