58 stories
The B2B market shifted completely. Local business owners don't care about simple landing pages anymore—they care about cutting operational costs. If you can connect ChatGPT to a local business's CRM using Make.com, you can easily charge a $2,000/mo retainer. The best part?
Friday afternoons. For years, they’ve been synonymous with one thing for me: the weekly project update compilation. You know the drill: trawl through Jira tickets, peek at Confluence pages, copy-paste snippets, and then try to condense a week’s worth of chaos into a few palatable bullet points for…
As web scraping becomes more complex, the tools we use to navigate that complexity need to become smarter. I recently took some time to explore how Claude skills can be integrated into a web scraping workflow.
On Tuesday you and your agent spent twenty minutes deciding not to keep sessions in Redis: the team would rather run one less service, and Postgres already handles the load. On Friday, in a new conversation, the same agent suggests moving sessions to Redis. It is not being stubborn.
Every batch job I run against LLM APIs used to cost the same at 2 a.m. as at 2 p.m. Then I started routing workloads through a unified gateway that charges different rates at different hours, and my overnight evaluation runs dropped to half the token cost — without touching a single prompt.
Sidekick Part 2: grounding beats instructions — facts injected before the model sees the prompt Small local models have a habit nobody warns you about: they ignore system-prompt rules.
DIf you're a developer dreaming of working abroad in the UK, USA, Canada, or Australia, you’ve probably hit this wall: You spend 3 hours going through a coding challenge, ace the recruiter screen, and then hear: "Sorry, we only consider candidates who already have full right to work without…
A prompt injection defense that works assumes the model will lose. You can't reliably stop an LLM from following instructions hidden in an email or a web page.
Everyone's started auditing the first bill. Almost nobody has found the second one. Here's what I mean. Open your agent's context and count what's already loaded before you type a single character. There are two line items, and they come from two different places: MCP tool schemas.
Keeping a model locally also lets you manage generation settings locally. But an output image and a model name do not tell you which workflow ran, with which parameters, on which environment.
A CrewAI crew writes our daily pharma brief for three cents One daily Pharma brief from a three-agent CrewAI crew costs $0.030 on Claude Haiku 4.5 when the tool hands the model the news API's key sentences: 14,452 prompt tokens, 3,042 completion tokens, 4 LLM calls, 8 stories with 8 real links.
Nobody budgets for the runaway loop. Every AI SaaS has a line item for "expected LLM spend" and nobody has a line item for "the Friday night a bug turned our agent into a money printer." I've seen the second one. Here's the pattern that prevents it.
RAG stands for retrieval augmented generation. Strip the jargon and it is one idea: the model does not know your documents, so before you ask it a question, you look up the relevant passage yourself and paste it into the prompt. Retrieve, augment, generate. That is the whole trick.
Somewhere in your finance or ops team, someone is opening a bank statement PDF right now and typing numbers into a spreadsheet by hand. Maybe it is a reconciliation task. Maybe it is a lending application, and a human is scanning twelve pages of transactions looking for red flags.
Introduction Computers cannot read words. They only understand numbers. A word embedding turns a word into a list of numbers. Python example model = api.load("glove-wiki-gigaword-50")
The associative half is easy to show in a few lines of Python. Store each note with the concepts it is about, count how often two concepts are written together, and let a read follow the strong pairs, so a question about timeouts brings back a note about backoff that never mentions a timeout.
Disclosure: I work on NeuralSound, one of the three tools tested here. The method, the raw numbers and the caveats are all below, so you can judge them yourself or rerun the test. Every AI vocal remover says it's "studio quality". Almost none of them publish numbers.
You know the feeling. Pager goes off at 2 AM. You stumble to your laptop, open the terminal, and stare at a PostgreSQL stack trace you've seen before — but can't remember exactly what fixed it last time. You dig through Slack threads, closed Jira tickets, and a half-finished runbook.
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