Saturday, October 17, 2026

Google Cloud VPC Networking – Detailed Explanation

 # Google Cloud VPC Networking – Detailed Explanation


In Google Cloud, a Virtual Private Cloud (VPC) is a logically isolated network where you deploy resources such as Compute Engine VMs, GKE clusters, databases, and internal applications.


A VPC provides control over IP addressing, connectivity, routing, security, DNS, and access to private services.


One important difference from AWS is that Google Cloud VPC networks are global, while their subnets are regional.


## 1. Understanding the overall architecture


Consider a production application with a frontend, FastAPI backend, PostgreSQL database, and private access to Google Cloud services.


Conceptual architecture. Cloud SQL uses service networking or Private Service Connect, rather than being an ordinary VM inside your application subnet.


## 2. The nine VPC networking sections


1\. Subnets


A subnet is a regional IP address range within a VPC. VMs and other supported resources receive their internal IP addresses from a subnet.


Example: `10.10.1.0/24` in Mumbai (`asia-south1`), with VM addresses `10.10.1.10` and `10.10.1.20`.


Google Cloud automatically creates subnet routes. Unlike AWS, subnets are not inherently public or private based on an associated route table. Internet reachability depends on external IPs, routes, firewall rules, and related connectivity.


2\. Static Internal IP Addresses


These are private IP addresses reserved for consistent use. Unlike an automatically allocated ephemeral internal IP, a reserved address can be retained for reuse, subject to its reservation type and scope.


Example: reserve `10.10.1.50` for an internal application VM or internal load balancer.


Useful when other applications depend on a stable private endpoint. Internal addresses may be regional or global, depending on the resource.


3\. Firewalls


VPC firewall rules allow or deny network traffic based on direction, source or destination, protocol, port, priority, and target.


Example: allow TCP port `443` to an application while restricting SSH port `22` to approved administration sources.


Rules can target VMs through network tags or service accounts. Google Cloud also supports hierarchical and network firewall policies for centralized management.


Firewall rules are stateful: permitted connection response traffic is generally allowed automatically.


4\. Endpoint Groups


Network endpoint groups (NEGs) represent collections of endpoints that can serve as backends for load balancers.


Endpoints might be VM IP-and-port combinations, GKE Pod endpoints, serverless services, or internet endpoints, depending on NEG type.


Example: an application load balancer distributes requests to three FastAPI backends using a NEG.


Important: A Network Endpoint Group is not the same as a Private Service Connect endpoint or a Cloud NGFW firewall endpoint.


5\. Firewall Endpoints


Cloud NGFW firewall endpoints are managed resources used for advanced Layer 7 inspection, including capabilities such as intrusion prevention and TLS inspection when appropriately configured.


These endpoints are associated with VPC networks and integrated with the applicable firewall policies.


Example: inspect outbound application traffic for known malicious signatures.


A standard VPC firewall rule does not require a firewall endpoint.


6\. Routes


Routes determine where IP packets travel to reach their destination.


Common route types include subnet routes, static routes, and dynamically learned routes through Cloud Router.


Example: `10.10.1.0/24` is a subnet route, while `0.0.0.0/0` may direct traffic toward the default internet gateway.


Routes determine the network path, but they do not independently authorize traffic. Firewall rules and other connectivity conditions still apply.


7\. VPC Network Peering


VPC Peering provides private IP connectivity between two VPC networks, including networks in different projects or organizations.


Example: a production application in `vpc-app` communicates with internal services in `vpc-shared`.


Peering is non-transitive: if VPC A peers with B, and B peers with C, A does not automatically reach C.


Subnet ranges must not conflict, and firewall rules must permit the intended traffic.


8\. Private Service Access


Private Service Access (PSA) enables private connectivity to supported Google-managed service producer networks using private services access connections.


Example: configure Cloud SQL private IP access using an allocated address range and a private service connection.


Your application connects to the database using private addressing rather than a public database endpoint.


PSA is different from Private Service Connect (PSC), which provides endpoint-based private access to supported services.


9\. DNS Configuration


DNS converts domain names into IP addresses. Cloud DNS supports private DNS zones, forwarding, and other resolution configurations.


Example: `api.internal.example.com` resolves to `10.10.1.20`.


Applications can use service names rather than hardcoded IP addresses. Private DNS zones can be authorized for specific VPC networks.


## 3. How these components work together


Imagine deploying your FastAPI multi-agent application with a PostgreSQL database and a load balancer.


| Requirement                                   | Google Cloud networking component |

| --------------------------------------------- | --------------------------------- |

| Allocate private IPs to application VMs       | Subnets                           |

| Give a backend a stable private IP            | Static Internal IP                |

| Permit HTTPS and restrict SSH                 | Firewall rules                    |

| Register backend endpoints for load balancing | Endpoint Groups (NEGs)            |

| Inspect traffic for advanced threats          | Firewall Endpoints                |

| Decide how packets reach destinations         | Routes                            |

| Connect application and shared-service VPCs   | VPC Network Peering               |

| Access supported managed services privately   | Private Service Access            |

| Resolve internal service names                | DNS Configuration                 |


## 4. Important differences between AWS VPC and Google Cloud VPC


Since AWS networking concepts are closely related, this comparison is useful.


| Feature                              | AWS                                       | Google Cloud                                     |

| ------------------------------------ | ----------------------------------------- | ------------------------------------------------ |

| VPC scope                            | Regional                                  | Global                                           |

| Subnet scope                         | Availability Zone                         | Region                                           |

| VM-level firewall                    | Security Groups                           | VPC firewall rules/policies                      |

| Subnet-level filtering               | Network ACLs                              | No direct equivalent                             |

| Routing                              | Route tables associated with subnets      | VPC routes and policy-based routes               |

| Private network connectivity         | VPC Peering                               | VPC Network Peering                              |

| Private managed-service connectivity | PrivateLink / service-specific networking | Private Service Connect / Private Service Access |

| DNS                                  | Route 53 Resolver                         | Cloud DNS                                        |

| Advanced traffic inspection          | AWS Network Firewall                      | Cloud NGFW                                       |


## 5. Recommended hands-on exercise


For learning, create a small VPC called `demo-vpc` in Google Cloud and configure the following:


VPC setup checklist


Interactive


0 of 8 completed


Reset


Create a custom-mode VPC named demo-vpc


Create a subnet 10.10.1.0/24 in asia-south1


Create two Compute Engine VMs in the subnet


Reserve a static internal IP for one VM


Create a firewall rule allowing required internal traffic


Inspect automatically created subnet routes


Configure a private DNS zone and record


Test private communication between the VMs


This exercise covers the foundational VPC features. VPC Peering, Private Service Access, endpoint groups, and advanced firewall endpoints can be explored afterward because they require additional resources or services.


One key concept: VPC networking is not just about connecting resources. It separates responsibilities: subnets allocate addresses, routes determine paths, firewalls control permitted traffic, DNS resolves names, and private connectivity mechanisms link networks and services.


For a production AI application, understanding these distinctions is particularly important when designing private connectivity between GKE, Compute Engine, Cloud SQL, Vertex AI, and internal services.


Official reference: [Google Cloud VPC documentation](https://cloud.google.com/vpc/docs/vpc?utm_source=chatgpt.com).



Tuesday, October 13, 2026

What is Colossus

 Colossus is Google’s proprietary, exabyte-scale distributed file system and the direct successor to the original Google File System (GFS). [1] (https://en.wikipedia.org/wiki/Google_File_System), [2] (https://abhishekkumar2718.github.io/programming/2020-03-15-google-file-system.html), [3] (https://siliconangle.com/2021/04/19/google-shares-technical-overview-exabyte-scale-colossus-file-system/)


Core Purpose & Scale


• Underpinning Infrastructure: It serves as the core storage backbone for Google's internal services (like YouTube, Gmail, and Google Search) and Google Cloud offerings (like Cloud Storage, BigQuery, and Cloud SQL). [1] (https://cloud.google.com/blog/products/storage-data-transfer/a-peek-behind-colossus-googles-file-system), [2] (https://www.infoq.com/news/2021/04/google-colossus/), [3] (https://abhishekkumar2718.github.io/programming/2020-03-15-google-file-system.html)

• Real-Time & Batch Workloads: While GFS was built mainly for batch processing, Colossus is architected to handle both low-latency, real-time services and heavy batch workloads simultaneously. [1] (https://research.google.com/pubs/archive/44877.pdf), [2] (https://abhishekkumar2718.github.io/programming/2020-03-15-google-file-system.html)

• Borg Integration: Unlike GFS—which ran on dedicated master machines—Colossus runs natively inside Google's Borg cluster management infrastructure. [1] (https://news.ycombinator.com/item?id=39756262), [2] (https://cloud.google.com/blog/products/storage-data-transfer/a-peek-behind-colossus-googles-file-system)


Key Architectural Components


Colossus breaks down its tasks into specialized distributed pieces:

• Curators: The control plane metadata servers that handle file creation, deletion, and directory operations. They scale horizontally and store metadata inside Google's Bigtable, allowing the system to scale over 100 times larger than legacy GFS clusters. [1] (https://cloud.google.com/blog/products/storage-data-transfer/how-colossus-optimizes-data-placement-for-performance), [2] (https://techfieldday.com/video/the-latest-in-high-performance-storage-rapid-on-colossus-with-google-cloud/), [3] (https://www.scribd.com/document/829043634/Colossus-Google-s-File-System), [4] (https://www.infoq.com/news/2021/04/google-colossus/)

• Custodians: Maintenance workers that handle data durability, replication, disk space balancing, garbage collection, and RAID reconstruction. [1] (https://www.infoq.com/news/2021/04/google-colossus/), [2] (https://cloud.google.com/blog/products/storage-data-transfer/how-colossus-optimizes-data-placement-for-performance), [3] (https://techfieldday.com/video/the-latest-in-high-performance-storage-rapid-on-colossus-with-google-cloud/)

• "D" File Servers: A massive fleet of network-attached disk servers (HDDs and SSDs) that store the actual raw bytes of files. [1] (https://techfieldday.com/video/the-latest-in-high-performance-storage-rapid-on-colossus-with-google-cloud/), [2] (https://www.infoq.com/news/2021/04/google-colossus/), [3] (https://cloud.google.com/blog/products/storage-data-transfer/how-colossus-optimizes-data-placement-for-performance)

• Colossus Client Library: The entry point for applications; it talks to Curators to resolve file handles and metadata, then communicates directly with D servers for data transfer (minimizing physical network hops). [1] (https://www.storagenewsletter.com/2025/04/14/google-cloud-colossus-secret-ingredient-in-rapid-storages-high-performance/), [2] (https://www.infoq.com/news/2021/04/google-colossus/), [3] (https://techfieldday.com/video/the-latest-in-high-performance-storage-rapid-on-colossus-with-google-cloud/)

Would you like to explore how Colossus compares to other distributed file systems like HDFS or how it integrates with Google Cloud Storage?



What is pmsampsize?

 


1. What is pmsampsize?

pmsampsize (Prediction Model Sample Size) is a statistical procedure used to calculate the minimum sample size needed to develop a reliable multivariable prediction model.

It was developed from research by Riley and colleagues on sample-size requirements for clinical prediction models.

Unlike traditional sample-size methods that focus on testing a single hypothesis, pmsampsize considers whether the dataset is sufficiently large to develop a model with an acceptable risk of overfitting.

It supports three types of prediction outcomes:

- Binary: Two possible outcomes, such as PHQ-9 ≥ 10 versus PHQ-9 < 10.

- Continuous: A numerical outcome, such as total PHQ-9 score.

- Time-to-event: An outcome involving the time until an event occurs.

For your NHANES capstone, you used the binary-outcome planning procedure because your machine-learning models predict whether a participant has elevated depressive symptoms.

2. Why is pmsampsize important for your capstone?

Your Research Question 3 investigates whether demographic, socioeconomic, lifestyle, and health-related factors can predict elevated depressive symptoms.

You are comparing Logistic Regression, Random Forest, and XGBoost.

Before developing these models, you need to answer:

Do we have enough participants and enough positive cases to develop a reasonably stable prediction model?

Simply having thousands of records is not sufficient. For example, if only 20 participants have the outcome, fitting a model with many predictors could produce unreliable results.

pmsampsize helps address this by considering outcome prevalence, model complexity, expected predictive performance, and overfitting.

Monday, October 12, 2026

What is AI Slop

AI slop is low-quality, mass-produced digital content created using generative artificial intelligence that lacks real effort, meaning, or human oversight. [1] (https://en.wikipedia.org/wiki/AI_slop), [2] (https://www.youtube.com/shorts/tEJpzzfpJEE)

The term functions as a pejorative slang label—similar to online spam—and was selected as the 2025 Word of the Year by both Merriam-Webster and the American Dialect Society. [1] (https://en.wikipedia.org/wiki/AI_slop)


Core Characteristics of AI Slop


• High Volume, Low Value: Produced rapidly and in large quantities, often to game social media algorithms, chase ad revenue, or drive clicks and engagement. [1] (https://www.coursera.org/articles/ai-slop), [2] (https://www.youtube.com/shorts/sU32pKKxb68), [3] (https://www.weforum.org/videos/ai-slop/), [4] (https://en.wikipedia.org/wiki/AI_slop), [5] (https://www.youtube.com/shorts/tEJpzzfpJEE)

• Three Main Types:

• Text: Repetitive phrasing, generic or shallow statements, lack of specific evidence, and an emotionless tone (such as generic blog posts or poorly written books).

• Images and Art: Unnatural or plastic textures, distorted hands, faces, or anatomy, bizarre/absurdist mashups (like Jesus fused with prawns), and melted or garbled text.

• Videos: Unnatural movement, synthetic computerized voiceovers, and absurd scenarios (such as kittens in pools of rainbow goo). [1] (https://www.merriam-webster.com/dictionary/slop), [2] (https://www.coursera.org/articles/ai-slop)


Why It Matters


• Clutters Feeds: It floods search engines, social media timelines, and video platforms, making it harder to find authentic, human-created material.

• Spreads Misinformation: Unchecked AI generation can mimic reality or create deepfakes, confusing users—particularly older audiences who may share it without realizing it is fake.

• Erodes Trust: It breeds general skepticism toward online media and brands that rely on automated, low-effort content. [1] (https://www.youtube.com/shorts/1tiyaxwzg7Y), [2] (https://www.charityexcellence.co.uk/ai-for-charities-slop/), [3] (https://www.facebook.com/AARPLatino/videos/should-we-be-worried-about-ai-slop/1425251762398182/), [4] (https://www.coursera.org/articles/ai-slop), [5] (https://www.youtube.com/shorts/tEJpzzfpJEE)

You can read more about how the dictionary defines the phenomenon on the Merriam-Webster Dictionary Entry or explore guidance on spotting it through the Coursera Guide on AI Slop. [1] (https://www.merriam-webster.com/dictionary/slop), [2] (https://www.coursera.org/articles/ai-slop)

Would you like help learning how to spot AI slop in a specific medium (like images, articles, or video), or are you looking for tips on how creators can avoid making it?


What is GcPing and Performance dashboard

 


gcping is an open-source utility (available as both a website and a command-line tool) that measures network latency from your device to various Google Cloud Platform (GCP) regions around the world. [1] (https://github.com/googlecloudplatform/gcping)

However, gcping is now deprecated and shutting down. Users are officially instructed to transition to the Google Cloud Performance Dashboard, which is the exact tool described in your provided Google Cloud documentation link. [1] (https://www.gcping.com/)


1. What was gcping?


Developed originally as a community side-project and later hosted under the Google Cloud Platform GitHub repository, gcping.com allowed users to open a browser tab and immediately see a real-time list of median ping times (latency) to Google’s global data centers. It achieved this by sending localized HTTPS requests to small Google Cloud Run services deployed across different regions. Developers used it to quickly choose the best Google Cloud region for deploying their apps or to troubleshoot baseline internet lag. [1] (https://gcping.com/?ref=mikkipastel.com), [2] (https://github.com/imjasonh/gcping), [3] (https://www.libhunt.com/compare-gcping-vs-cloudping.co), [4] (https://github.com/googlecloudplatform/gcping)


2. What is the Performance Dashboard?


The Google Cloud Performance Dashboard is a robust, enterprise-grade module inside the Network Intelligence Center. Unlike the simple outside-in check of gcping, the Performance Dashboard provides granular visibility into the actual packet loss and network latency of your live cloud architecture. [1] (https://docs.cloud.google.com/network-intelligence-center/docs/performance-dashboard/concepts/overview), [2] (https://medium.com/google-cloud/network-performance-diagnostics-with-gcp-performance-dashboard-52d93bc85a02)

It breaks network observability into two main views:

• Project Performance View: Focuses exclusively on your own infrastructure. It tracks metrics for your Virtual Machine (VM)-to-VM traffic, as well as traffic moving between your cloud instances and the internet locations of your actual end-users.

• Google Cloud Performance View: Shows global, macro-level zone-to-zone packet loss and latency across the entire Google Cloud infrastructure. You don't need resources deployed in these areas to see this data.


Key Differences at a Glance



Feature gcping (Deprecated) Performance Dashboard (Official Replacement)

Primary Scope Outside-in latency from your browser/machine to GCP. Intra-cloud performance (VM-to-VM) and Cloud-to-Internet metrics.

Metrics Captured Median HTTP latency only. Packet loss averages and Latency (RTT) medians.

Data History Live instant snapshot. Up to 6 weeks of historical tracking logs.

Workers & Probes Browser HTTPS requests to Cloud Run endpoints. Infrastructure-level probe workers that do not consume your VM resources.

Actionability Manual observation. Supports custom alerting policies and Cloud Monitoring dashboard exports.



Why use the Performance Dashboard over a ping tool?


The primary advantage is Performance Diagnostics. If your live application suddenly slows down, you can cross-reference your project's latency charts with the global Google Cloud network view. If a latency spike aligns perfectly with a regional Google Cloud infra anomaly, you immediately know the root cause lies in the underlying network provider, instantly saving hours of software troubleshooting. [1] (https://docs.cloud.google.com/network-intelligence-center/docs/performance-dashboard/concepts/use-cases-google-cloud)

Would you like help navigating to the Performance Dashboard inside the Google Cloud Console, or are you looking to set up alerting thresholds for your existing project workloads?



AI-XF — AI eXchange Format

A portable, vendor-neutral format for curated knowledge that both humans and AI agents produce and consume. It adds what a reasoning agent needs on top of a folder of markdown: stable identity, typed relationships, provenance, media identity, federation, and change semantics.


AI-XF is a strict superset of Google Cloud's Open Knowledge Format (OKF) v0.2. Every AI-XF bundle is also a valid OKF bundle: OKF-only agents read it today, AI-XF-aware agents read the same files and see more.


Spec SPEC.md — v0.5 released; v0.6 proposed (one resolution order across bundles)

Experiments experiments/ — runnable evidence: federation (E1–E7), freshness and interoperability (E8–E9), maturity journeys into a database, from a wiki and into another system (E10–E12), canonical form and withheld links (E13–E14), resolution order with an independent implementation (E15); numbers in RESULTS.md

Curation policy (non-normative) CURATOR.md — seven rules and four numbers to paste into an agent's instructions

Worked example examples/ — passes the validator at Level 3

Validator tools/ai-xf-validate.py — conformance ladder, --stats (curation health and freshness), --federation, --resolve (explains how one link resolves); no dependencies

Canonical form (v0.5) tools/ai-xf-canon.py — writes the one canonical serialisation of a concept and its sha256: content hash (SPEC §5.7, Appendix D); needs PyYAML



The idea in one paragraph

Keep knowledge as markdown files with YAML frontmatter, exactly as OKF says. Give each concept a permanent id so files can move. Give each link a rel so an agent knows whether B replaces A or B depends on A. Carry OKF's trust fields unchanged so a human-reviewed concept means the same thing in every bundle. Give binary assets a content hash so a diagram keeps its identity when it moves. Let bundles from different teams cite each other by namespace/id. Record what happens when new knowledge meets old: an open contradiction, a merge, a claim gaining support. And when a concept leaves, keep it as a tombstone that says so, rather than deleting it. Everything stays "just files"




Sunday, October 11, 2026

Few rules that can be used for RAG and agents

 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.


Friday, October 9, 2026

What is TCREI framework

 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)




What is Hermes OKF?

 


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.


Wednesday, October 7, 2026

Jev Integration with Langchain

 



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.")


Jev In detail

 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.

Saturday, October 3, 2026

What is custom Auth provider in Langgraph

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)

 

Wednesday, September 30, 2026

What is RAgraph

 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

What is Apache Solr

 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.


What is Jev

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.





Saturday, September 26, 2026

What is Muse AI?

 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)


The Agent harness concept

"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.



Harness As a Service vs Self managed 


 The model is the engine. It is where the power comes from. But nobody ships a bare engine bolted to a pallet to a customer. The chassis, brakes, dashboard, and seatbelts are what make it something you’d put your family in. The model is only the engine; it is everything that makes the product safe, reliable, and pleasant to use is the harness you build around it.

The Two Halves of a Harness

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

Friday, September 25, 2026

What is Radar and why Radar for Kunernetes

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




Thursday, September 24, 2026

What is Thin MCP

 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)


Monday, September 21, 2026

What is WorkIQ?

 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)