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@faun shared a link, 7 hours ago

My Own DNS Server At Home

RunningBIND on Fedora with Podmanputs you in the driver’s seat—local DNS, full zone control, and no third-party middlemen. It handles staticforward/reverse zonesacross multiple IPv4 subnets, skips the mess of dynamic updates, and plugs into your router as a recursiveforwarding resolver. Call it a se..

My Own DNS Server At Home
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@faun shared a link, 7 hours ago

Le Chat now integrates with 20+ enterprise platforms—powered by MCP—and remembers what matters with Memories.

Le Chat now includes20+ secure, MCP-based connectorsfor tools like GitHub, Snowflake, Stripe, and Jira. That means in-chat search, summaries, and actions—straight from enterprise systems. Developers can plug in their owncustom MCP connectors, and run Le Chat wherever it fits: on-prem, private cloud..

Le Chat now integrates with 20+ enterprise platforms—powered by MCP—and remembers what matters with Memories.
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@faun shared a link, 7 hours ago

OpenAI to launch its first AI chip in 2026 with Broadcom, FT reports

OpenAI’s firstin-house AI chipis nearly out of the oven. It’s headed for fabrication atTSMCand built to handle OpenAI’s own workloads—no outside sales, according to theFinancial Times. Why it matters:Big AI shops are going vertical. Custom silicon means tighter control over runtime, reliability, an..

OpenAI to launch its first AI chip in 2026 with Broadcom, FT reports
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@faun shared a link, 7 hours ago

Why language models hallucinate

OpenAI sheds light on the persistence ofhallucinationsin language models due to evaluation methods favoring guessing over honesty, requiring a shift towards rewarding uncertainty acknowledgment. High model accuracy does not equate to the eradication of hallucinations, as some questions are inherentl..

Why language models hallucinate
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@faun shared a link, 7 hours ago

From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels

Hugging Face just dropped Kernel Builder—a full-stack toolchain for building, versioning, and shippingcustom CUDA kernels as native PyTorch ops. Kernels arearchitecture-aware,semantically versioned, andpullable straight from the Hub. It tracks changes with lockfiles and bakes inDocker deploysout of..

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@faun shared a link, 7 hours ago

GPT-5 Thinking in ChatGPT (aka Research Goblin) is shockingly good at search

GPT-5's“thinking” modeljust leveled up. It's not just answering queries—it’s doing full-on research. Picture deep, multi-step Bing searches mixed with tool use and reasoning chains. It reads PDFs. Analyzes them. Suggests what to do next. Then actually does it. All from your phone. What’s changing:L..

GPT-5 Thinking in ChatGPT (aka Research Goblin) is shockingly good at search
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@faun shared a link, 7 hours ago

The Big LLM Architecture Comparison

Architectures since GPT-2 still ride transformers. They crank memory and performance withRoPE, swapGQAforMLA, sprinkle in sparseMoE, and roll sliding-window attention. Teams shiftRMSNorm. They tweak layer norms withQK-Norm, locking in training stability across modern models. Trend to watch:In 2025,..

The Big LLM Architecture Comparison
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@faun shared a link, 7 hours ago

Best Practices for High Availability of LLM Based on AI Gateway

Alibaba Cloud’s AI Gateway just got sharper. It now handlesreal-time overload protectionandLLM fallback routingusing passive health checks, first packet timeouts, and traffic shaping. It proxies both BYO and cloud LLMs—think PAI-EAS, Tongyi Qianwen—and redirects load spikes or failures on the fly. F..

Best Practices for High Availability of LLM Based on AI Gateway
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@faun shared a link, 7 hours ago

Hermes V3: Building Swiggy’s Conversational AI Analyst

Swiggy just gave its GenAI tool, Hermes, a serious glow-up. What started as a simple text-to-SQL bot is now acontext-aware AI analystthat lives inside Slack. The upgrade? Not just tweaks—an overhaul. Think: vector-based prompt retrieval, session-level memory, an Agent orchestration layer, and a SQL..

Hermes V3: Building Swiggy’s Conversational AI Analyst
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@faun shared a link, 7 hours ago

Simplifying Large-Scale LLM Processing across Instacart with Maple

Instacart builtMaple, a backend brain for handling millions of LLM prompts—fast, cheap, and shared across teams. It’s not just another service. Maple runs onTemporal,PyArrow, andS3, strip-mines away provider-specific boilerplate, auto-batches prompts, retries failures, and slashes LLM costs by up t..

Simplifying Large-Scale LLM Processing across Instacart with Maple

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