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Your Angular bundle is bigger than it needs to be. 📦 Here’s a 3-step cleanup you can do today: 1️⃣ Run npx depcheck to find packages you never use 2️⃣ Check any new library on bundlephobia.com before installing it 3️⃣ Run ng build --stats-json and set budgets in angular.json

1/5 Angular 22 is out, and it's all about removing everyday friction. 7 features worth knowing 🧵 2/5 Templates get closer to TypeScript. Spread, arrow functions, and instanceof now work in templates: <button (click)="count.update(v => v + 1)">+1</button> And @switch now supports stacked

@case fallthrough, so cases can share one block. Signals reach the router and resources. isActive() returns a signal, and Router.isActive() is deprecated Resource snapshots let you transform and compose resources like any other signal 4/5 Easier debugging and migration provideStabilityDebugging

Static LLM leaderboards are useless for agents. A model ranked #1 in general chat will break your app if its tool-calling reliability drops or TTFT spikes during high-load hours. We added real-time benchmarking to our LLM Recommender: Live latency & throughput tracking

Garba beats on loop, vibrant colours, and an infectious energy in the air! Navratri is finally here. May these nine divine nights fill your home with positivity, peace, and endless joy. Time to dust off the dandiya sticks and embrace the festivities! Shubh Navratri to you and your loved ones!

Voice agents in demos: • Clean audio • Zero background noise • No interruptions • <500ms latency Voice agents in production: • 8kHz phone audio • Barking dogs & bad cell signals • Dynamic barge-in handling • Complex state & API timeouts The demo sounds magical.

1/7 Most AI agents suffer from acute amnesia or vector rot. Stuffing 1M tokens into context is expensive, and vanilla RAG only retrieves semantic similarity—not chronology. If you want agents that handle long-horizon tasks, you need a Temporal Graph Memory Harness. Here’s how to architect one:

6/7: Query Routing & Traversal Retrieval is hybrid: Vector Seed: Find initial entry-point entity nodes via cosine similarity on the prompt. Time-Constrained Traversal: Hop $k$-degrees outward along edges where valid_to >= target_epoch. Output deterministic, temporal subgraphs instead of unranked,

1/7 Most AI agents suffer from acute amnesia or vector rot. Stuffing 1M tokens into context is expensive, and vanilla RAG only retrieves semantic similarity—not chronology. If you want agents that handle long-horizon tasks, you need a Temporal Graph Memory Harness. Here’s how to architect one:

Entity/Fact Layer: Deduplicated entities resolved across sessions via dynamic fuzzy/vector linking. Community/Procedural Layer: Aggregated behavioral heuristics & patterns ("every time X fails, retry via Y"). 5/7: The Async "Dream Cycle" (Ingestion & Consolidation)

1/7 Most AI agents suffer from acute amnesia or vector rot. Stuffing 1M tokens into context is expensive, and vanilla RAG only retrieves semantic similarity—not chronology. If you want agents that handle long-horizon tasks, you need a Temporal Graph Memory Harness. Here’s how to architect one:

Time is not metadata; it is a first-class dimension. 3/7: Bi-Temporal Edge Modeling Every graph relation $E = (S, P, O)$ must be stamped bi-temporally: Valid Time ($t_v$): When the fact was true in reality. Transaction Time ($t_x$): When the agent learned it.

1/7 Most AI agents suffer from acute amnesia or vector rot. Stuffing 1M tokens into context is expensive, and vanilla RAG only retrieves semantic similarity—not chronology. If you want agents that handle long-horizon tasks, you need a Temporal Graph Memory Harness. Here’s how to architect one:

Why learn angular Zero guesswork: The CLI and schematics automate boilerplate and enforce a standard project architecture. All-in-one tooling: Routing, HTTP client, and form handling work seamlessly together without external library fatigue.

Want to build an AI agent in JavaScript that actually browses the web for ground truth? 🌐🤖 Here’s the core architecture in 4 steps: 1️⃣ User Query ➔ LLM Router: Detects if external real-time data is needed. 2️⃣ Tool Call Execution: Triggers a web search API (Tavily/SerpAPI) or headless scraper.

3️⃣ Context Ingestion: Strips boilerplate HTML, extracts clean text, and chunks relevant snippets. 4️⃣ Synthesis Loop: Injects the search results back into the model's prompt for a cited, hallucination-free answer. No magic, just deterministic tool calling + clean context injection.

Never ask an AI coding assistant to write your business logic and test suite at the same time. It will just test its own hallucinations. Write the strict Vitest/Jest assertions FIRST. Then instruct the model: "Make these tests pass." Instant verification loop 👇

Collecting Kotlin Flows directly with `collectAsState()` in Jetpack Compose keeps streams active in the background, draining resources. Always use `collectAsStateWithLifecycle()` to pause consumption when the app goes into the background. Code comparison 👇

Firestore Data Modeling Cheat Sheet for Developers! Don't let bad NoSQL schema design inflate your cloud bill. Here is how to choose between Arrays, Subcollections, and Root Collections: 🔹 Embedded Arrays ➔ Use for small, bounded data (<100 items) like roles or tags. Subcollections ➔

Stop writing prompt hacks like "Respond ONLY in valid JSON, no markdown backticks" for Gemini. Set `responseMimeType: "application/json"` and pass a strict `responseSchema`. You get 100% deterministic, type-safe objects ready for JSON.parse(). Code card attached 👇 #GeminiAI #GoogleCloud

How to Structure an AI Agent Workflow (2026 Developer Blueprint) Moving from prompt engineering to agentic architecture? Here is the standard multi-agent pipeline you need to know. 📌 Key Stages: 1️⃣ Input Parsing 2️⃣ Tool & Function Calling 3️⃣ Database Execution (Firestore/Cloud)

Do you know about Raw and transfer size in angular? Raw size is the actual file size of main.js on your disk before any network compression happens. It shows the true weight of the code you wrote or compiled. #typescript #programming #nodejs #react #webdeveloper #reactjs #softwaredeveloper

Join the Builders Club | Practical Dev Community We’re building a dev community focused 100% on real-world projects and shipping practical code—zero fluff, no theory, no endless tutorials. What we do: Build and ship real products together in short sprint cycles Level up practical skills

Ollama now supports DeepSeek Harness (dsh), DeepSeek's open-source agent harness. Ollama's local and cloud models work with it out of the box without extra configuration. Download Ollama, then run: ollama launch dsh If DeepSeek Harness isn't already installed, Ollama installs it and

information, Ollama handles the search and returns results directly — no MCP servers or API keys required. #typescript #programming #nodejs #react #webdeveloper #reactjs #reactnative #softwaredeveloper #software #vuejs #php #jquery #mongodb #mysql #webdesigner #frontend #softwareengineering

Ollama now supports DeepSeek Harness (dsh), DeepSeek's open-source agent harness. Ollama's local and cloud models work with it out of the box without extra configuration. Download Ollama, then run: ollama launch dsh If DeepSeek Harness isn't already installed, Ollama installs it and

Want to stop answering the same questions about your code? Ship a context checklist or skill with your next PR. Whether you're handing work to a teammate or an AI agent, use this exact structure to guarantee alignment: Why this exists Name the exact problem and the constraint that shaped the work.

Don't assume they know the backstory. 🧠 Main decision Explain what you chose and why. More importantly, state what you rejected and why it didn't make the cut. ✅ Checked List the proof: • The critical behavior • The failure or edge case • Exact steps for someone else to reproduce it