A continuous-monitoring content engine that watches sixty-plus technology sources, generates source-grounded ideas, drafts in the voice of five real engineers, scores every draft on five dimensions, and ships only when a human approves. We use it to publish 2-5 pieces a week — and yes, the name is on purpose.
Ascentspark is a thirty-person technology shop with eleven years of delivery experience. Five members of our leadership team have deep domain knowledge and distinct voices — and none of them has time to write consistently. Hiring a content team was considered and rejected. The problem was never the lack of writers. It was the absence of a system that could continuously monitor the technology world, generate ideas grounded in real events, draft in the authentic voice of specific team members, and apply quality controls rigorous enough that a human reviewer could approve with confidence rather than rewrite from scratch.
Blabberfish is what we built. It runs on us. We publish from it. We named it to keep ourselves honest — if a system can fool you into thinking a real engineer wrote a piece, you should at least name it something that admits what it is.
Spiders 60+ curated sources across the technology ecosystem. Filters for relevance against our domain. Deduplicates against published and proposed content. Generates 5-10 fresh ideas. Writes them to the Idea Bank for a human to approve.
Competitive research, outline, drafting in the chosen author's persona, fact-check, AI-detection test, 5-dimension QC loop (up to 3 rounds), SEO metadata, multi-platform social kit. Output: a publication-ready Google Doc with everything a human needs to review and publish.
Polled via TwitterAPI.io — ~$0.15 per 1K tweets, 100K free credits to start. We score every fetched item 1-10 for relevance to Ascentspark's domains before it can become an idea.
YouTube Data API v3 for metadata, transcripts when captions exist, Gemini 1.5 Pro for multimodal summarisation when they don't. The system understands video content without watching it.
RSS feeds where they exist, HTTP scraping with semantic HTML extraction where they don't. Posts are tagged by source and filtered against the Published Articles baseline.
GitHub API hit per-topic — agents, llmops, vector-database, rag, multi-modal, etc. Trending repos under a star threshold are demoted; rising new entrants are surfaced.
Critical and high-severity CVEs scoped to the technologies our clients run. We do not chase low-severity noise — but a high-severity in a framework we ship gets surfaced immediately.
Trending-model API + downloads-per-week deltas. A model jumping from 5K to 50K weekly downloads is usually a story worth covering.
Heavy reasoning lands on Claude Opus — exactly twice per pipeline. Balanced work goes to Claude Sonnet. Sub-second structured-extraction tasks go to gpt-4o-mini. The cost discipline is a routing decision, not a prompt-engineering trick.
Writes the full article body in Markdown. 8000 max tokens. Highest-quality model — used exactly once per pipeline.
Every draft is scored on tone fit, factuality, usefulness, engagement, and humanisation. If any dimension falls short, the system diagnoses the specific problem, applies a targeted fix, and re-scores. The loop is capped at three rounds — past that, the piece escalates to a human. The operator sees the round counter and knows whether the article passed first-try or was force-published.
A judge loop without a cap is a bill nobody signed up for. A cap without a judge ships first drafts. We do both — the loop is the quality engine, the cap is the cost engine, and the operator sees both as plain numbers on the timeline.
Every author persona is a real Ascentspark team member. Each has a system prompt that captures their sentence rhythm, their vocabulary, their humour style, their technical stance — even their banned words. A drafted piece sounds like the person on the byline because the model is operating under a tightly-scoped voice contract. Pick an author below.
Direct, no-padding prose. Technical precision as non-negotiable. Dry sarcasm. Sceptical-but-evolving stance on AI — 'AI is like fire. A good servant, but a bad master.'
The drafting prompt also enforces a humanisation contract: varied sentence rhythm, occasional fragments, banned AI-indicator phrases, no em-dashes, no corporate buzzwords, natural digression, and a probabilistic typo rule — 70% chance of zero typos, 30% chance of one or two minor punctuation slips. Never the same pattern twice in a row.
Blabberfish runs on a stack that is deliberately boring — Google Sheets as the operational database, Google Docs as the content delivery format, OpenClaw as the scheduler, N8N as the workflow engine, Claude as the intelligence layer. We add custom code only where it earns its keep. The fanciness is in the prompts, the personas, and the QC loop.
The architecture pattern is the same — your sources, your personas, your domains, your quality bar. The five-dimension QC, OpenClaw scheduling, source-grounded ideation, and persona-authentic drafting all transfer. The only thing we cannot supply is the voice itself — that has to come from the people who actually have one.
If you're thinking about AI for your business and you want to talk to someone who will tell you the truth — including when AI is the wrong answer — book a call. We'll spend 30 minutes understanding what you're trying to do, what you're worried about, and whether we're the right fit.