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My Honest Advice for Aspiring Machine Learning Engineers

Becoming a machine learning engineer requires dedicatingat least 10 hours per weekto studying outside of everyday responsibilities. This can take a minimum of two years, even with an ideal background, due to the complexity of the required skills. Understanding core algorithms and mastering the funda.. read more  

My Honest Advice for Aspiring Machine Learning Engineers
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Automatically Evaluating AI Coding Assistants with Each Git Commit · TensorZero

TensorZerotransforms developer lives by nabbing feedback fromCursor'sLLM inferences. It dives into the details withtree edit distance (TED)to dissect code. Over in a different corner,Claude 3.7 SonnetschoolsGPT-4.1when it comes to personalized coding. Who knew? Not all AI flexes equally... read more  

Automatically Evaluating AI Coding Assistants with Each Git Commit · TensorZero
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Linux 6.16 Performance Regression Tracked Down In New Futex Code

Linux 6.16takes a36% performance nosediveon AMD EPYC 9005 all thanks toFUTEXPRIVATEHASH. The quick fix? Yank it. Engineers scramble for a smarter solution... read more  

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Critical Linux “sudo” flaw allows any user to take over the system

Millions of Linux systems are vulnerable to a sudo flaw allowing unauthorized users to run commands as root. The bug affects Ubuntu and Fedora servers, escalates privileges to root, and requires installation of the latest sudo packages for mitigation. The flaw lies in the seldom-used sudo chroot fea.. read more  

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Grafana Tempo 2.8 release: memory improvements, new TraceQL features, and more

Grafana Tempo 2.8lands with a bang. Say hello toTraceQL query hints—they bump up results you care about and streamline span searches with parent span IDs. Meanwhile,compactor poolingrevamps slashes memory usage. Kiss those OOM errors goodbye. Important heads-up:serverless features are historyand the.. read more  

Grafana Tempo 2.8 release: memory improvements, new TraceQL features, and more
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Atlassian moved 4 million Postgres databases to AWS Aurora

Atlassianpulled off a major coup, relocating 4 million Jira Postgres databases toAWS Aurora. They slashed expenses by taming CPU beasts and carved out a rock-solid 99.99% uptime. A delightful efficiency cocktail. SamsungandTSMCare brooming through some project cobwebs. Samsung's rethinking its Texas.. read more  

Atlassian moved 4 million Postgres databases to AWS Aurora
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Hewlett Packard Enterprise completes $14B acquisition of Juniper after settlement of DOJ suit

Hewlett Packard Enterprise closed its acquisition of Juniper Networks following the settlement of a lawsuit by the U.S. Department of Justice. This acquisition will allow HPE to expand its networking business and compete in the AI networking market. HPE officials stated that the merger positions the.. read more  

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Serving 200 million requests per day with a cgi-bin

UsingGoandRustwith CGI-style requests taps into multi-core CPU might, poking fun at long-held CGI inefficiency myths... read more  

Serving 200 million requests per day with a cgi-bin
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Understanding Network Packet Offsets & Safe Parsing in eBPF

eBPFandRustteam up to drive a network packet parser that catches packets at breakneck kernel speed. Welcome to the future of observability and security.XDPsteps in, slicing latency to the bone for real-time inspection... read more  

Understanding Network Packet Offsets & Safe Parsing in eBPF
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OpenYurt Becomes a CNCF Incubating Project

OpenYurt, a CNCF brainchild, shakes up cloud-edge orchestration. It dances with Kubernetes like Fred Astaire and partners with any vendor under the sun... read more  

OpenYurt Becomes a CNCF Incubating Project
DeepSeekMath-V2 is a state-of-the-art mathematical reasoning model built on the DeepSeek-V3.2-Exp-Base architecture with 685 billion parameters. Unlike conventional math-focused language models that optimize only for correct final answers, DeepSeekMath-V2 introduces a self-verification framework where the model generates, inspects, and validates its own mathematical proofs.

This approach enables rigorous, step-by-step reasoning suitable for theorem proving, scientific research, and domains requiring high-integrity logic. The model is trained through a generation-verification loop involving a dedicated LLM-based verifier and reinforcement learning optimized for proof correctness rather than answer matching.

DeepSeekMath-V2 achieves gold-level scores on IMO 2025 and CMO 2024, along with a groundbreaking 118/120 on the Putnam 2024 contest. Released under the Apache 2.0 license and hosted on Hugging Face, it is fully open source for research and commercial use.