Yggdrasil Gaming Reduces Data Costs 60% with AWS Lakehouse

Amazon Web Services (AWS) just posted result of our work on their Linkedin page. It's pleasure to be noticed. Data... Data... Data... Data... must be structured. 🚀🚀🚀

We are giving away a LOT to the AI. Feeding them like crazy. I am afraid of this honestly and becoming more and more interested in local setups. I don’t want to feed a cloud AI with my own realizations and insights.

Nothing says “trust our AI” like a late-night email about data settings. Serious engineers are moving toward Local AI on owned GPUs:: your code stays yours, your context stays local and your model serves you, not the vendor. The AI split is becoming obvious: Cloud: rent intelligence, donate context. Local: own intelligence, keep context.

For all Github users, Github will start using your code to train their models, unless you opt out before April 24th.

🎮 How Game District Can Leverage GCP for High-Scale Gaming Analytics 🚀 Ever wondered how modern gaming companies handle millions of player events in real time? I recently designed this architecture to show how a company like Game District can use Google Cloud Platform (GCP) to build a scalable, data-driven system 👇 🔹 Ingestion Layer Real-time player events (gameplay, purchases, ads) flow through Pub/Sub / Kafka 🔹 Processing Layer Dataflow processes streaming data → cleans & transforms it 🔹 Storage & Lakehouse Cloud Storage + BigLake → flexible data lake BigQuery → high-performance analytics 🔹 Business Insights Layer This is where the magic happens: 👉 Retention (Day 1 / Day 7 / Day 30) 👉 Engagement (DAU, MAU, Session Duration) 👉 Monetization (ARPDAU, LTV) 👉 Live Ops Dashboards (real-time KPIs) 🔹 Security & Monitoring IAM, VPC, Data Catalog, Monitoring → ensuring reliability & governance 💡 Key Takeaway: In gaming, data is not just collected — it’s used to optimize player experience and maximize revenue in real time. As a Data Engineer, I focus on building systems that: ✔ Scale to billions of events ✔ Deliver low-latency insights ✔ Enable better business decisions Would love to hear your thoughts — how would you design this differently? 👇 #DataEngineering, #BigQuery, #GCP, #GamingAnalytics, #DataPipeline, #CloudComputing, #RealTimeData, #Analytics, #ETL, #DataLake, #Kafka, #PubSub,

Google announces Gemma 4 open AI models, switches to Apache 2.0 license https://lnkd.in/gptexZQy #InSubcontinent #Google #announces #models, #switches #Apache #license INSWorld

LM Studio running Qwen 3.5 9B on RTX 2060 — exposed via OpenAI-compatible API. Hooked that endpoint into an agent flow and tested code generation directly inside VS Code. Model selection, response latency, token generation . I'm testing everything locally. No external API calls. No cloud dependency. Still early, but offline agentic workflows are starting to feel usable. I'm hoping for Google's new compression algorithm Turboquent. That's better then all 4bit quantaiz model #buildinpublic #localai #llm #opensource #ai #lmstudio

Google just turned Colab into a programmable sandbox for AI agents! No more copy-pasting code into a colab notebook on a browser tab. The new Colab MCP Server gives your local agents (like Claude Code or Gemini CLI) direct, programmatic access to Colab’s cloud GPUs and runtimes. Key Points: - Direct GPU Access: Offload heavy compute from your laptop to the cloud via CLI. → Self-Correction: Agents see the kernel state and errors, allowing them to debug and fix code autonomously. - Persistent Context: Agents build real .ipynb notebooks with documentation and logic, not just chat blocks. - The "agentic" workflow is here. Stop managing notebooks and start orchestrating them. Full analysis: https://lnkd.in/gxzxU4w4 Repo: https://lnkd.in/gezChjwN Technical details: https://lnkd.in/gePViDG8 Google Google AI for Developers

For those who need affordable processing for OpenAI OSS 20B models, I recommend Lightning.ai. 1 million tokens for $0.01. Not sure whether it is the cheapest, but definitely worth a try. The annoying thing is the rate limit: 15 requests/second. May need to apply multiple accounts for massive data processing.

Google just leveled the playing field with Gemma 4. The AI race isn't just happening in the cloud anymore—it's moving to our laptops and pockets. I just published a deep dive into Google’s newest "open" model family, Gemma 4. From native audio support to massive 256K context windows, here’s why this matters for developers and tech enthusiasts alike. Read my full breakdown on Medium: https://lnkd.in/g_dVwM2P #AI #GoogleGemma #TechInnovation #OpenSource #GenerativeAI