This technical talk explores LLM vulnerabilities, detailing attacks like Policy Puppetry and computational graph backdoors. Learn how to secure AI systems against sophisticated threats.
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Discover SQL MCP Server: The open-source solution for secure data access. It provides a standardized API layer to connect AI agents and modern apps to your enterprise database, simplifying development and enforcing security.
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Learn how Tessl's Dark Factory ships 70% of its code autonomously using AI agents, sophisticated verifiers, and a robust orchestration layer.
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Security flaws are often behavioral, not just technical. Learn how to use behavioral economics and secure defaults to redesign processes and build systems that guide developers toward secure code.
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Discover how LLMs fail because they predict tokens, not execute code. Learn about prompt injection, Base64 encoding bypasses, and advanced defenses like 'Spotlighting' to secure your AI systems.
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Llama.cpp and vLLM are two key engines for local LLMs. Llama.cpp optimizes for consumer hardware using quantization and GGUF files, while vLLM focuses on production scale with continuous batching and paged attention.
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Learn how Netflix built an AI agent to automate performance engineering. Discover how LLMs can read profiling data, find quadratic-time anti-patterns, and propose fixes validated by real production traffic.
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Harness engineering is a discipline focused on building automated loops of checks and maintenance agents to delegate more work to AI. This process involves improving three dimensions: autonomy, automation, and quality.
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AI News & Strategy Daily | Nate B Jones
DeepSeek V4 Pro charges just 87 cents for a million tokens, compared to Kimi K3 at $15. Evaluating Chinese models requires focusing on 'cost per accepted result,' not just token price.
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Anthropic may not be stealing your data, but their use of anonymized aggregate usage data creates a 'market map,' allowing them to compete directly with their customers. Learn how to achieve AI sovereignty.
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Moving beyond simple output checks, effective AI agent evaluation requires measuring trajectory and skill activation. Learn how structured context improves code quality metrics like PR throughput and adherence to unique business IP.
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Paid plans unlock private workspaces with versioning and lineage, SSO (SAML/OIDC), audit logs, and service accounts. Compute is billed separately from storage.
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DeepSWE is a contamination-resistant benchmark featuring 113 original software engineering tasks. It tests LLMs on long-horizon problems by focusing on observable behavior rather than PR-derived implementations, revealing critical gaps in model capabilities.
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AI News & Strategy Daily | Nate B Jones
Learn how to shift your AI automation strategy from merely answering tickets faster to eliminating the problem entirely by identifying and resolving root causes. We detail the 2026 approach.
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Data's value lies in the trajectory and reasoning trace, not just the final output. The speaker argues that successful companies must pivot from being mere 'data businesses' to becoming infrastructure providers (neo-labs).
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AI systems operate on statistical pattern prediction (syntactic), lacking true semantic understanding of intent or value. The human must remain the critical interpreter to ensure outputs are ethically grounded.
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AI adoption shifts bottlenecks from coding speed to coordination and alignment. Success is measured by the merge rate of PRs opened by non-technical users, proving that empowering decision-makers is key.
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Scaling LLMs requires sophisticated synthetic data pipelines, rigorous weight hashing checks, and fixing numerical precision bugs (BF16/FP32) to build models like Laguna S (118B parameters).
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Building a safe AI coach for mental health requires more than just LLMs; it demands an eval-driven playbook. SonderMind details its modular, clinically grounded architecture and how they integrate human expert judgment directly into their CI pipeline.
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