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| 🔗 Stories, Tutorials & Articles |
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| Agentic AI and Security |
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Agentic LLM apps come with a glaring security flaw: they can't tell the difference between data and code. That blind spot opens the door to prompt injection and similar attacks.
The fix? Treat them like they're radioactive. Run sensitive tasks in containers. Break up agent workflows so they never juggle all three parts of the “Lethal Trifecta”: sensitive data, untrusted input, and outbound access. And for now, keep humans in the loop - every loop. |
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| Why GPUs accelerate AI learning: The power of parallel math |
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Modern AI eats GPUs for breakfast - training, inference, all of it. Matrix ops? Parallel everything. Models like LLaMA don’t blink without a gang of H100s working overtime.
Teams now design training and inference pipelines around horizontal + vertical GPU muscle by default. |
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| New trend: Programming by kicking off parallel AI agents |
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Senior engineers are starting to spin up parallel AI coding agents - think Claude Code, Cursor, and the like - to run tasks side by side. One agent sketches boilerplate. Another tackles tests. A third refactors old junk. All at once.
Is it "multitasking on steroids"? Not just this as it messes with how devs plan, review, and ship. You can’t wing it when three agents are building in parallel. You need clear task slices, solid validation steps, and a tight feedback loop to keep the outputs sane. |
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| Grokipedia |
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Grokipedia just dropped - a Wikipedia remix built from LLM output, pitched as an escape from "woke" bias. The pitch? Bold. The execution? Rough.
Entries run long. Facts bend. Citations wander. And the tone? Cold, context-free, and unmistakably machine-made. The usual LLM suspects are here: hallucinations, lopsided coverage, and awkward sourcing.
When ideology drives generation, trust gets murky and governance grows teeth. |
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| Detect inappropriate images in S3 with AWS Rekognition + Terraform |
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| A serverless AWS pipeline runs image moderation on autopilot - with S3, Lambda, Rekognition, SNS, and EventBridge all wired up through Terraform. When a photo gets flagged, it’s tagged, maybe quarantined, and triggers an email alert. Daily scan? Handled. |
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| Build AI Agents Worth Keeping: The Canvas Framework |
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MIT and McKinsey found a gap the size of the Grand Canyon: 80% of companies claim they’re using generative AI, but fewer than 1 in 10 use cases actually ship. Blame it on scattered data, fuzzy goals, and governance that's still MIA.
A new stack is stepping in: product → agent → data → model. It flips the old ML workflow on its head. Start with the user problem. Build the agent. Then wrangle your data. Only then reach for the model. |
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