Reducing hallucinations in retrieval-augmented chatbots for customer support teams

When customer support teams adopt retrieval-augmented generation (RAG) to power chatbots, the promise is compelling: fast, contextually-aware answers grounded in a company's own documentation. In practice, however, one problem keeps surfacing — hallucinations. These are fluent, plausible-sounding responses that confidently state incorrect facts or invent citations. I've worked with product and security teams who’ve felt that a seemingly small hallucination can erode trust faster than any...

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Reducing hallucinations in retrieval-augmented chatbots for customer support teams
AI

Choosing a self-hosted vector database for on-device llm search: milvus, pgvector or chroma?

09/06/2026

When I started evaluating self-hosted vector databases for on-device LLM search, I expected a straightforward tradeoff: pick the fastest engine and...

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Choosing a self-hosted vector database for on-device llm search: milvus, pgvector or chroma?
Cybersecurity

Detecting malicious firmware implants on consumer routers using a raspberry pi and free tools

03/06/2026

I recently spent a week building a cheap, repeatable workflow to detect malicious firmware implants on consumer routers using nothing more than a...

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Detecting malicious firmware implants on consumer routers using a raspberry pi and free tools

Latest News from Roctoken Co

How to run a privacy-preserving fine-tuned llm on a raspberry pi 5 without cloud costs

I wanted to run a useful, private large language model (LLM) from my home lab without paying recurring cloud bills or leaking sensitive data to third parties. After a few evenings of tinkering I got a workflow that works reliably on a Raspberry Pi 5: fine‑tune (or adapt) a model on my local workstation, quantize it, and serve a compact, privacy-preserving instance on the Pi. In this guide I’ll walk you through the practical steps,...

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How to vet third-party SDKs before integrating them into consumer apps

I remember the first time I shipped an app that pulled in a third‑party SDK. It promised analytics, crash reporting and a couple of slick UI widgets — all in one package. The integration was painless and the demo looked great. A week later we started seeing unexpected traffic spikes, unexplained permissions prompts, and a client worried about leaked PII. That experience taught me to treat SDKs like components of my attack surface, not just...

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Choosing between Redis, PostgreSQL, and RocksDB for real-time analytics pipelines

I build and analyze data systems for a living, and one of the recurring questions I get from engineering teams and startups is: “Which storage should we pick for our real‑time analytics pipeline — Redis, PostgreSQL, or RocksDB?” I’ve spent time prototyping pipelines with all three, tuning them under load, and pushing them into production. Below I share a pragmatic, experience‑based guide to help you choose the right tool depending on...

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How to detect stealthy IoT devices on your home network using free tools

Quiet devices are the worst kind: they blend into your home network like wallflowers until something goes wrong. Over the last few years I’ve spent a lot of time hunting down “stealth” IoT gadgets — cameras that phone home on odd ports, smart bulbs that appear under generic hostnames, and devices that never show up in the router GUI. Below I’ll walk you through practical, free techniques and tools I use to find, fingerprint and monitor...

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Why your firmware updates fail and how to make device upgrades reliable in the field

I’ve spent years testing devices, pushing firmware images over flaky networks, and waking up to devices bricked by a half-applied update. Firmware updates are where the rubber meets the road for security, reliability and user trust — and they’re also where product teams make mistakes that turn manageable risks into expensive field failures. In this piece I’ll walk through why firmware updates fail in the real world and share concrete...

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A hands-on guide to securing open Wi‑Fi in coworking spaces without breaking usability

I spend a lot of time working from coffee shops, libraries and coworking spaces, and one question keeps coming up from readers, founders and friends: how do you secure devices and data on an open Wi‑Fi network without turning every connection into a fortress that destroys usability? In this hands‑on guide I walk through the practical steps I use to protect myself and my team in shared spaces. No theoretical laundry list — just workable...

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Comparing on‑device speech recognition engines for offline dictation workflows

When I moved several long-form writing workflows entirely offline, the single biggest friction point was reliable, accurate dictation that respected privacy and worked without an internet connection. Cloud ASR (automatic speech recognition) is great for accuracy, but for sensitive notes, interviews, or fieldwork where connectivity is spotty, on-device speech recognition is the only realistic option. I spent months evaluating and integrating...

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Practical privacy audit: what Google, Apple, and Microsoft really collect from your phone

I started this practical privacy audit because I got tired of vague privacy promises from big tech and wanted something I could apply to my own phone in under an hour. If you carry a smartphone from Google, Apple or Microsoft, you’re handing that company a lot of signals about your life—even when you think you’ve turned everything off. Below I walk through what these companies actually collect, how to find the evidence on your device and...

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How to set up cost-aware autoscaling for a machine learning inference API

I run inference APIs for models of different sizes — from tiny classification services to multi-GPU transformer endpoints — and one problem always comes up: how do I keep latency predictable without blowing the budget? Autoscaling is the obvious answer, but naïve autoscaling that only looks at CPU or request rate often leads to oscillation, over-provisioning, or surprise bills. In this guide I’ll walk you through a practical, cost-aware...

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How to test startup product-market fit using guerrilla usability sessions and metrics

I test product-market fit (PMF) the hard way: not by running expensive cohort studies or waiting for months of traction, but by getting prototypes and ideas in front of real people fast. Over the years I’ve leaned on guerrilla usability sessions — short, focused interviews and hands-on trials in informal settings — combined with a small set of actionable metrics. This combo tells you whether people understand, value and will pay for what...

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