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1. Introduction As the golden standard of secure remote access , the Secure Shell (SSH) protocol has several layers of protection. One of them involves recording and keeping track of the known servers on the client side. known_hosts By default, the known_hosts file for a given user is located at: cat /home/user_name/.ssh/known_hosts github.com ssh-rsa *** github.com ecdsa-sha2-nistp256 *** github.com ssh-ed25519 *** Basically, the file contains a list with several columns, separated by whitespace: Identifying host data Host key type Host key value Optional comment The first column can be hashed or cleartext, depending on the setting of HashKnownHosts in /etc/ssh/ssh_config . When hashed, the first field of each line starts with |1| , a HASH_MAGIC marker. After the latter, the field continues with a random 160-bit string, otherwise known as a salt, followed by a 160-bit SHA1 hash. Each of these is encoded in base64 . The main idea is to hide the IP address or hostname data, which would otherwise be directly visible Either way, known_hosts contains a mapping between a server as identified by its characteristics and its key . ## Known Hosts Checking When connecting to a remote host, SSH checks the known_hosts file of the client to confirm the address or hostname for the server match the key we get from it . If there is a match, the session setup can continue. Otherwise, we get an error. The entry for 192.168.6.66 in the known_hosts file doesn’t match the (Elliptic Curve Digital Signature Algorithm, ECDSA ) key we got back from the server at that address . Critically, if we don’t know what caused the error, we should heed the text in capital letters: something nasty can indeed be happening . On the other hand, the reasons for such an issue can be valid and trivial: dynamic IP address changed hostname reinstalled system reinstalled SSH Docker container misconfigured DHCP relocated client In fact, there can be many more. ## Bypass Known Hosts The error text when connectin

In This Article The Deal Intelligence Gap Data Model Design Transcript Analysis with Claude Automated Follow-Up Drafting PostgreSQL JSONB Storage Putting It Together The Deal Intelligence Gap Most CRM systems are excellent at storing what happened — call logged, email sent, stage updated — and poor at capturing what was learned. A sales call produces qualitative intelligence that is genuinely valuable for deal strategy: what objections surfaced, how strongly the prospect signaled interest, what next steps were agreed to, and what risk flags the conversation revealed. That intelligence almost never makes it into the CRM because it requires someone to spend 15 minutes synthesizing unstructured notes into structured fields. Large language models change this equation. Given a call transcript, Claude can extract structured deal intelligence in seconds — categorizing sentiment, identifying specific objections, recommending stage movement, and flagging risk signals — with accuracy that equals or exceeds what a well-trained sales analyst would produce manually. Data Model Design The data model centers on two tables. The deals table stores core deal attributes as a JSONB column, which allows flexible schema evolution without migrations as the intelligence fields change over time. The deal_activities table records each interaction — calls, emails, meetings — with the raw content in TEXT and the extracted intelligence in a separate JSONB column. A GIN index on both JSONB columns enables fast attribute queries across the deal pipeline. CREATE TABLE deals ( id UUID PRIMARY KEY DEFAULT gen_random_uuid (), company TEXT NOT NULL , contact TEXT , stage TEXT , attributes JSONB DEFAULT '{}' , created_at TIMESTAMPTZ DEFAULT now (), updated_at TIMESTAMPTZ DEFAULT now () ); CREATE TABLE deal_activities ( id UUID PRIMARY KEY DEFAULT gen_random_uuid (), deal_id UUID REFERENCES deals ( id ), activity_type TEXT , raw_content TEXT , intelligence JSONB , created_at TIMESTAMPTZ DEFAULT now () )

Token Budgeting: Optimizing Generative AI Costs and Performance Modern generative AI applications offer unprecedented capabilities, yet their operational costs can quickly escalate. The primary driver of these costs, alongside computational resources, is token consumption . Understanding and implementing effective token budgeting strategies is not merely an optimization; it is fundamental to building scalable, efficient, and economically viable AI systems. The Economics of Tokens Tokens are the atomic units of text that large language models (LLMs) process. Whether you're sending a prompt (input tokens) or receiving a response (output tokens), each token incurs a cost. This cost varies by model, but the principle remains: more tokens mean higher expenses and often, increased latency due to longer processing times. Efficient token management directly impacts your application's bottom line and user experience. Strategic Pillars of Token Efficiency Optimizing token usage requires a multi-faceted approach, focusing on both input and output, as well as the underlying model choices. 1. Input Optimization: Crafting Smarter Prompts The most direct way to save tokens is to be judicious with the information sent to the model. Every word in your prompt counts. Concise Prompt Engineering : Avoid verbose instructions or unnecessary conversational filler. Get straight to the point. Instead of: "Hey AI, I was wondering if you could please help me summarize this really long article I have here. It's about quantum computing. Could you make it brief, maybe just a few sentences?" Opt for: "Summarize the following article about quantum computing in three sentences: [Article Text]" This significantly reduces input tokens without sacrificing clarity. Context Window Management : LLMs have a finite context window , the maximum number of tokens they can process at once. Sending an entire document when only a specific section is relevant is wasteful. Employ techniques like: Summarization : P

This is a complete guide with steps by step process to deploy the gRPC and how to use Azure API Management to import the gRPC API. It cover step‑by‑step guide to deploying a gRPC API on Azure API Management (APIM), grounded in the Microsoft documentation and a real-world deployment workflow. NOTE: This post is published already in GITHUB here. https://github.com/shailugit/apimGrpc/blob/main/README.md The API Management can expose gRPC services, but with important constraints: APIM supports gRPC by importing a .proto file and forwarding calls to a gRPC backend. gRPC requires HTTP/2 end‑to‑end. gRPC APIs are supported in Self-hosted gateway and not supported in APIM v2 tiers. You can't use the test console to test gRPC The major steps claissfied in two major steps Creating a gRPC server Calling the gPRC application using APIM 1. Creating gRPC Application Typical backend deployment steps include the following Create a .NET gRPC server application Create a .NET gRPC client application Test the setup locally Publish the .NET gRPC server to Azure WebApp and verify the service works directly over HTTPS Step-1 As a first step we will be building a .NET gRPC server application. You can skip this step in case you already have gRPC server application. If you would like to view .NET Core sample used for this sample project, please visit here . Step-2 As a second step we will be building a .NET gRPC client application. You can skip this step in case you already have gRPC client. If you would like to view .NET Core client used for this sample project, please visit the below here . Step-3 Once your client and server code is ready here are the steps to Test your application locally Step-4 Deploy the server to Azure WebApp To understand how-to deploy a .NET 6 gRPC app on App Service, please visit here . Please make sure to enable HTTP version, Enable HTTP 2.0 Proxy and add HTTP20_ONLY_PORT application setting as gRPC only work using http2.0 as shown below 2. Calling gRPC from APIM T

INTRODUCTION Configuring a Power BI semantic model involves refining data structures, creating relationships, and setting up calculations. Semantic model is the last stop in the data pipeline before reports and dashboards are built. It is the end product of the raw data that has been extracted, transformed, loaded, modeled, built relationship, and written calculation. The Semantic model consist of Data connections to one or more data sources, Transformations that clean and prepare the data for reporting, Defined calculations and metrics based on business rules to ensure consistent reports and Defined relationships between tables. Key words to note in Semantic Modelling are; 1. Fact table and Dimension table: The Fact table records the quantitative and numerical data. It is where every single details are recorded. The Dimension table act as the descriptive companion to the fact table, containing the attributes or characteristics that provide context to the data. 2. Primary and Foreign Key: Primary Keys are unique identifier assigned to a specific record with a database table ensuring that no two rows are identical or repeated. foreign Keys are columns or group of columns in one table that provides a link between data in two tables by referencing the primary key of another. 3. Star Schema Star Schema is a data modeling technique where a central fact table is surrounded by several dimension tables that provide descriptive content. 4. Cardinality Cardinality defines the kind of relationship between two tables. They are; One to Many (1.*) Many to one (*.1) One to One (1.1) Many to Many ( . ) The cardinality of a relationship is described by the "one" (1) or "many" (*) icons located at the ends of the relationship line. 5. Cross Filter Direction The direction determine how filters propagate. Possible cross filter options are dependent on the relationship cardinality type. One to Many - Single or Both sides One to One - Both sides Many to Many - Single to either table or b

A group of 20 Snap alumni has come together to launch a fund called Ghost Angels to back the next generation of social media.

The golden age of Microsoft's Github Copilot appears to be at an end.

Happy to announce HeliosProxy !! Far beyond a pooling tool, HeliosProxy ** is a next-gen programmable Postgres data-plane. **Works with PostgreSQL-compatible databases , not only HeliosDB. It starts as a PgBouncer-compatible wedge, then adds the operational surface teams usually build from multiple tools: connection pooling failover and transaction replay shadow execution anomaly detection edge cache controls admin REST API embedded admin UI signed WASM plugins OCI-style plugin artifacts Kubernetes operator Terraform and Pulumi providers 22 installable Claude/Codex operator skills Install operator skills: heliosdb-proxy install skills PostgreSQL #DevOps #SRE #Database #AIcoding

Event sourcing gets sold on immutability. You don't update, you don't delete, you only append, so the history is permanent. It mostly isn't. The events are immutable because your code agrees not to touch them, not because anything actually stops it. Underneath they're still rows in Postgres, and rows have a DBA with write access. A migration that "cleans up" old data. A 2 a.m. query run against the wrong connection. A backup restored with slightly different bytes in it. Change one of those rows and a replay won't blink. The aggregate rebuilds, the projections rebuild, everything looks fine. Usually the first person to notice is a customer whose balance is off, and by then the trail is cold. Chain each event into the next The trick is small. Give every row two extra columns: a hash of its contents, and the hash of the row before it. #1 AccountOpened prev=00000… hash=70be4f… │ ▼ #2 AmountDeposited prev=70be4f… hash=796018… │ ▼ #3 AmountWithdrawn prev=796018… hash=6a0260… The hash is SHA-256(previousHash || json(payload)) . Nothing exotic. The point is that each hash depends on the one before it. Edit a payload and its hash stops matching. Rewrite that hash to cover for the edit, and now the next row's pointer is wrong. You can't fix one without breaking the next. About forty lines of it Appending an event hashes it together with the previous one: public HashChainedEntry Append ( object payload ) { var previousHash = _entries . Count == 0 ? GenesisHash : _entries [^ 1 ]. Hash ; var hash = ComputeHash ( previousHash , payload ); var entry = new HashChainedEntry ( _entries . Count + 1 , payload , previousHash , hash ); _entries . Add ( entry ); return entry ; } internal static byte [] ComputeHash ( byte [] previousHash , object payload ) { var payloadJson = JsonSerializer . SerializeToUtf8Bytes ( payload , payload . GetType ()); var combined = new byte [ previousHash . Length + payloadJson . Length ]; Buffer . BlockCopy ( previousHash , 0 , combined , 0 , previousHash .

Microsoft is facing criticism for its handling of zero-day exploits. Someone going by the name Nightmare Eclipse has been publicly feuding with the company, posting proof-of-concept exploit code. Some of their posts suggest that they're a disgruntled former employee. But what caught cyber security researcher Kevin Beaumont's eye was how Microsoft has responded. Microsoft suggests […]

Most QR scanner libraries for React Native share the same problems — they're unmaintained, they don't support the New Architecture, or they pull in a full camera SDK for what is a single-feature module. I wanted something lean, production-grade, and built the right way. So I built it. This is react-native-qr-camera-pro — a QR and barcode scanner for React Native built entirely with native code. No JavaScript frame processing. No unnecessary dependencies. Swift on iOS, Kotlin on Android, TurboModules and Fabric throughout. Why Native-Only? The common alternative is running frame analysis in JavaScript — grabbing frames via a JS-accessible camera API and running a WASM or JS barcode decoder on them. It works, but it puts real pressure on the JS thread and limits your frame rate. With native-only processing: iOS uses AVCaptureMetadataOutput — Apple's own pipeline for detecting machine-readable codes. Frames are never copied to user space; the kernel hands off a reference to the same buffer. Android uses CameraX ImageAnalysis + ML Kit — Google's on-device barcode scanner backed by hardware-accelerated inference where available. The JS bridge is touched at most once every 500ms to deliver a result. Everything else stays native. Architecture The module is three layers: Architecture The module is three layers, each with a single responsibility: Layer What it does JavaScript / TypeScript Public API — QrCameraProView , startScanning() , stopScanning() , toggleTorch() , useBarcodeScanner() , useCameraError() Native Bridge (TurboModules + Fabric) Type-safe JSI communication between JS and native. Codegen spec drives both the iOS C++ adapter and the Android Kotlin stub. Native Platform (iOS + Android) All camera and barcode logic. AVFoundation on iOS, CameraX + ML Kit on Android. Zero JS involvement in frame processing. iOS (Swift) Class Responsibility QrCameraProSwift Owns the AVCaptureSession lifecycle BarcodeThrottler Throttle + dedup logic BarcodeTypeMapper Maps AVMetadataO

Si vous utilisez PostgreSQL, vous avez probablement déjà dû choisir entre une clé primaire BIGSERIAL et un UUID. Depuis des années, la version 4 (aléatoire) est le choix par défaut quand on veut un identifiant unique et distribué. Mais en 2026, une alternative plus récente s’impose : UUID v7, qui intègre un timestamp et promet de meilleures performances pour les index. Dans cet article, je vous explique concrètement ce qui change, avec des benchmarks PostgreSQL et des exemples de code, pour que vous puissiez décider en connaissance de cause. UUID v4 : le standard aléatoire et son problème d’index Un UUID v4 est constitué de 122 bits aléatoires. Cette absence totale de tri est sa force pour l’unicité, mais elle devient un handicap dans un index B‑tree, qui est la structure utilisée par PostgreSQL pour les clés primaires. Lorsque vous insérez un nouvel UUID v4, il a autant de chances de se retrouver au début de l’index qu’à la fin. Résultat : l’index se fragmente, les pages se remplissent mal, et les performances d’écriture se dégradent à mesure que la table grossit. J’ai reproduit un test simple sur PostgreSQL 16 avec 10 millions de lignes, en utilisant une table dont la seule différence est la colonne id : -- Table UUID v4 CREATE TABLE events_v4 ( id UUID DEFAULT gen_random_uuid () PRIMARY KEY , payload JSONB , created_at TIMESTAMPTZ DEFAULT now () ); -- Table UUID v7 (généré côté application, voir plus bas) CREATE TABLE events_v7 ( id UUID PRIMARY KEY , payload JSONB , created_at TIMESTAMPTZ DEFAULT now () ); Après insertion, voici les mesures : Type de clé Taille de l’index Fragmentation Latence moyenne d’insertion BIGINT ~214 Mo 0 % ~0,8 ms/ligne UUID v4 ~428 Mo (2×) 99 % ~4,8 ms/ligne UUID v7 ~428 Mo (2×) ~2 % ~1,1 ms/ligne Ce qui frappe, c’est la fragmentation quasi nulle de l’UUID v7. L’index reste compact et les insertions sont presque aussi rapides qu’avec un BIGSERIAL. L’UUID v4, lui, est plus de quatre fois plus lent à l’insertion sur ce volume. UUID v7 :

It's the world's worst kept secret that Nvidia is about to announce its own Arm-powered laptop chips at Computex this weekend, and now Microsoft, Nvidia, and Arm are all openly teasing the announcement. The Windows and Nvidia GeForce accounts on X both posted "A new era of PC" earlier today, and now Arm has followed […]

If you manage PostgreSQL databases across multiple environments, you've probably done this: SSH to the DB host (or connect via psql ) Run CREATE USER jsmith CONNECTION LIMIT 20 PASSWORD '...' Slack the password to the developer Forget to log it anywhere Repeat for every environment, every onboarding, every access request It's tedious, error-prone, and leaves zero audit trail. Here's a better way. What I Built pg-user-api is a lightweight Flask REST API that wraps PostgreSQL user provisioning in clean HTTP endpoints. You register your databases once in a SQLite inventory, then any tooling — CI pipelines, internal portals, Ansible playbooks, or a plain curl — can create and manage users across environments without ever touching psql . GitHub: pcraavi/PostgreSQL-user-creation-API The Problem It Solves In teams that span dev, QA, UAT, and prod, you end up with different patterns of users: App service accounts — named after the host/port combo ( web01_8080 ) Kubernetes workload accounts — named after env prefix + farm ( dv_gearservice ) Individual dev/QA accounts — low connection limits, scoped to non-prod Read-only analyst accounts — prod only, no DDL DBA accounts — CREATEDB CREATEROLE LOGIN , rarely provisioned Each type has different CONNECTION LIMIT values, privilege levels, and naming conventions. Encoding these patterns in an API means the rules are consistent, repeatable, and auditable. Architecture The project is intentionally small — five Python files and a requirements list: pg_user_api/ ├── app.py # Flask app — all endpoints ├── auth.py # HTTP Basic Auth (constant-time compare) ├── database.py # SQLite registry + audit log ├── notifications.py # Notification stubs (Webex / Slack / Email) ├── seed_db.py # One-time setup: creates DB + sample records └── requirements.txt Two credential pairs, clearly separated: PG_API_USER / PG_API_PASS — who can call this API (your team/tooling) PG_ADMIN_USER / PG_ADMIN_PASS — the PostgreSQL DBA role that executes DDL The DBA cr

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

The people deciding that AI can replace your job are also the ones least likely to understand what your job truly involves, according to Box founder Aaron Levie, who pointed to this as an example of “AI psychosis.” Indeed, ClickUp recently cut 22% of its workforce for AI agents, tech layoffs in 2026 are already nearly matching all of 2025, […]

A public spat between Microsoft and an independent security researcher reopens a long-running debate over who is responsible for securing software.

I pondered the other day what's next for Microsoft's Surface PC lineup, and it looks like we're about to find out. Windows and Surface chief Pavan Davuluri has just teased "something new is coming for developers," complete with a mysterious image of what looks like a curved display edge. Davuluri notes that whatever is coming […]

If you're using Amazon SES and emails to @privaterelay.appleid.com are silently failing, the cause is almost certainly SES's account-level suppression list treating Apple relay DSN errors as hard bounces. Fix: Emails Not Delivered to Apple Private Relay Addresses (Amazon SES) If your app supports Sign in with Apple , some of your users will have a Hide My Email address — a relay address like abc123@privaterelay.appleid.com that forwards to their real inbox. These addresses are easy to break silently. Here's the symptom we ran into and exactly how we fixed it. The Symptom Transactional emails (alerts, welcome emails) worked fine for regular email addresses but never arrived for users who signed in with Apple. We were also receiving bounce notifications like this: Subject : Delivery Status Notification (Failure) An error occurred while trying to deliver the mail to the following recipients: prw8xms8tv@privaterelay.appleid.com Confusingly, some of these emails were being delivered — Apple's relay occasionally returns a DSN error even on successful delivery. But over time, delivery stopped entirely for affected addresses. Root Cause: SES Suppression List Amazon SES has an account-level suppression list . When a send results in a bounce (even a soft or misleading one), SES adds that address to the suppression list and silently drops all future sends to it — no error, no log entry from your code's perspective. Apple's private relay sometimes returns a non-standard response that SES interprets as a hard bounce. Once that happens: Send to Apple relay → Apple returns DSN error → SES logs as hard bounce → Address added to suppression list → Every future send silently dropped We found 9 Apple relay addresses on our suppression list, the oldest suppressed since September 2025 — meaning those users had missed months of emails. The Fix Step 1 — Remove suppressed Apple relay addresses In the AWS Console : Go to Amazon SES (make sure you're in the correct region) Left sidebar → Con

Consoles with disc drives are the easiest way to enjoy all kinds of physical media, but that could end with the next-gen PlayStation 6 and Microsoft's Project Helix.