DevSecOps in Practice
A Hands-On Guide to Operationalizing DevSecOps at Scale
Updates and recent posts about LangChain.. Activity
@devopslinks added a new tool OWASP Dependency-Check , 1 month, 1 week ago.
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@varbear added a new tool pre-commit , 1 month, 1 week ago.
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@devopslinks added a new tool GitGuardian , 1 month, 1 week ago.
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@devopslinks added a new tool detect-secrets , 1 month, 1 week ago.
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@devopslinks added a new tool Gitleaks , 1 month, 1 week ago.
Course
@eon01 published a course, 1 month, 1 week ago
Founder, FAUN.dev
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@tairascott shared a post, 1 month, 1 week ago
AI Expert and Consultant, Trigma
How Do Large Language Models (LLMs) Work? An In-Depth Look
Discover how Large Language Models work through a clear and human centered explanation. Learn about training, reasoning, and real world applications including Agentic AI development and LLM powered solutions from Trigma.
Story
@laura_garcia shared a post, 1 month, 1 week ago
Software Developer, RELIANOID
🔐 RELIANOID at Gartner IAM Summit 2025 | Dec 8–10, Grapevine, TX
We’re heading to the Gartner Identity & Access Management Summit to showcase how RELIANOID’s intelligent proxy and ADC platforms empower modern IAM: enhancing Zero Trust enforcement, adaptive access, and hybrid/multi-cloud security. Join us to explore AI-driven automation, ITDR, and identity governa..
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@varbear shared a link, 1 month, 1 week ago
FAUN.dev()
Confessions of a Software Developer: No More Self-Censorship
A mid-career dev hits pause after ten years in the game -realizing core skills likepolymorphism, SQL, and automated testingnever quite clicked. Leadership roles, shipping products, mentoring junior devs - none of it filled those gaps. They'd been writingC#/.NETfor a while too. Not out of love, just .. read more
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@varbear shared a link, 1 month, 1 week ago
FAUN.dev()
Building a Blockchain in Go: From 'Hello, Block' to 10,000 TPS
A new Go tutorial shows how to build a lean, fast blockchain - clocking ~10,000 TPS - without the usual bloat. It covers the full stack:P2P networking,custom consensus, and properstate management. No unbounded mempools. No missing snapshots. Just a chain that actually runs, benchmarked on real machi.. read more
LangChain supports retrieval-augmented generation (RAG) pipelines through integrations with vector databases, enabling models to access and reason over large external knowledge bases efficiently. It also provides utilities for handling long-term context via memory management and supports multiple backends like OpenAI, Anthropic, and local models.
Technically, LangChain simplifies building LLM-driven architectures such as chatbots, document Q&A systems, and autonomous agents. Its ecosystem includes components for caching, tracing, evaluation, and deployment, allowing seamless movement from prototype to production. It serves as a foundational layer for developers who need tight control over how language models interact with data and external systems.
