Duncan S Anderson writes The Prompt Factory newsletter and shares analysis and practical experiments on AI models, agent systems, and the costs and trade-offs of running them. Anderson's posts cover topics such as making agent tools more reliable and evaluating smaller, lower-cost models.
Useful for technical content on agent harness engineering, model costs, and cost-efficient model selection for AI infrastructure buyers.
Best for
A B2B AI infrastructure company wanting technical content on agent harness engineering and cost-efficient model selection.
Not for
A consumer AI app growth team seeking paid creator installs through short-form video for a general audience.
Key takeaways
Moved a live agent from Claude Sonnet to cheaper Nemotron; it broke immediately.
Trace review showed failures came from the harness, not just the model.
A 27B model on a laptop shifts the data-centre debate.
Check first: Confirm terms, disclosure, analytics, and audience data; no rate card or inquiry route is published.
Main ideas
Near-miss model output is an engineering problem, not a reason to buy a bigger model · Cheaper inference is also an energy decision · A model that runs on a laptop undercuts the case for giant data centres
Duncan S Anderson writes The Prompt Factory newsletter and shares analysis and practical experiments on AI models and agent systems.
The work is especially useful for readers trying to understand what changes when they choose a model: how cost affects deployment, what smaller or open-weight models can do, and what can go wrong when an agent uses tools.
In one production example, a lower-cost model stopped after one search; tracing the failures led them to improve the harness rather than simply blame the model.
Other posts weigh laptop-sized models against data-centre requirements and track the spread of decision models while noting unresolved questions about calibration.
The Prompt Factory’s stated format—three important developments, selected links, and one practical action—offers a reason to follow: it favors argued analysis and tests over an unfiltered AI news roundup.
Duncan connects broad AI infrastructure debates to concrete choices and failure cases.
Rather than treating a model’s benchmark or price as the whole story, the posts examine how it behaves inside a system, what defensive engineering can compensate for, and what trade-offs remain.
A post about decision models highlights calibration and tooling as open problems, while a comparison involving Qwen presents a training-data claim as an allegation.
That combination of hands-on examples, cost and energy questions, and attention to caveats helps readers assess new AI developments without mistaking a promising result for a dependable system.
Duncan S Anderson argues that the AI industry's assumptions about what serious agent work costs — in money, hardware and energy — are looser than they look.
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They work through production failures in public, treat near-miss model output as a harness engineering problem rather than a reason to buy a bigger model, and run a newsletter built on a few argued points plus one action the reader can try.
Near-miss model output is an engineering problem, not a reason to buy a bigger model
Duncan moved a production agent from Claude Sonnet to a much cheaper small model, and it stopped working properly.
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The obvious first read was that small models cannot sustain multi-step tool use.
But traces showed almost every failure was mechanical: how tool calls were named, parameterised and parsed.
A harness can absorb those errors, so Duncan built one that expects the model to be "almost right" rather than paying for a cleverer model.
A failed model swap can reveal as much about the surrounding code as the model.
The agent called search instead of hybrid_search, a name that does not exist but blends names it had been shown.
It passed query as a parameter to a tool that takes name, filled an optional date field with the literal string "None" instead of leaving it empty, and typeset a citation with a non-breaking hyphen that quietly failed a pattern match in UI code.
A third harness design worked after two earlier ones failed.
Duncan describes a week when a library upgrade renamed a field and silently killed an output sanitiser.
Duncan treats token price as a proxy for compute, and compute as a proxy for power, making model routing an environmental choice as well as a budget decision.
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A fraction of the cost means a similar fraction of the power. The agent experiment shows this choice is available now that a cheaper model can be made to work.
Nvidia's Nemotron 3.5 Lightning costs ten cents per million input tokens against Claude Sonnet's two dollars — twenty times cheaper on input and forty times cheaper on output.
The post closes with a direct choice: "Save money or save energy.
A model that runs on a laptop undercuts the case for giant data centres
Duncan uses an independent benchmark of tool use, planning and multi-step problem solving — the capabilities that matter when building agents rather than chatbots — to make a hardware argument.
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A 27B parameter open-weight model high on that leaderboard, yet running on an ordinary laptop, challenges the assumption that serious agentic work requires enormous compute and the data-centre and power buildout that assumption justifies. Duncan notes an allegation that Alibaba used Anthropic model outputs to train Qwen, then sets it aside to make the wider claim.
Qwen3.8-27B ranks seventh on Artificial Analysis's agentic leaderboard.
It has an Apache 2.0 licence, open weights and needs no API key, so nothing leaves the machine.
Duncan puts the cost in CFO terms: over a quarter of Claude Opus's input cost and an eighth of its output cost.
Open replication outruns the hard parts of a new idea
Duncan tried a decision model in a chatbot routing layer, found it faster and cheaper, then watched the idea get copied from several directions within days.
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The excitement is real, but calibration and tooling remain unsolved.
A decision model is useful only if its stated 80% confidence actually means 80%.
The spread shows how fast open work moves, not that decision models are production-ready.
A developer on r/LocalLLaMA pointed out that Duncan had published essentially the same architecture with open weights in March last year.
Someone shipped openjev on Hugging Face; Jared Palmer at Vercel released Kev-0.5B, a Jev-compatible decision model that trains and runs on a laptop; Google's Gemma team showed DiffusionGemma doing the same trick; and Harrison Chase at LangChain said Jev had sparked more internal demos than any launch he could remember, including anything from OpenAI or Anthropic.
Duncan also points to a toy language built by a Cloudflare engineer, where if-statements are model judgements.
Duncan runs The Prompt Factory as a recurring slot built around a forced view, not a headline feed: a few argued points, links for everything else and one action the reader can complete.
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Picking three items is harder than picking six because it forces a position — and that position is the value. The format also suits a co-produced or sponsored edition: it already commits to an argument and a test.
The sample edition's view: renting AI got dearer and owning it got easier.
OpenAI halved the included usage on its $200 plan and Google cut its free tier, while open decision models got close enough to hosted versions to run on a laptop.
Barnacle Labs released Cyprid, an open-source way to try them on a Mac, that same week.
The recommended action is a test runnable in an evening.
Duncan cut the newsletter to about half the length of the previous week's and says it is better for it, adopting that edition's three-things-plus-one-action structure. The newsletter lands on Sundays.
Model access is becoming a question of nationality
Duncan follows a model takedown as a policy story rather than a product one, tracing how the order came about and what it means for anyone choosing a provider.
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The reported sequence — a jailbreak flag from Amazon's CEO, Amazon's stake in Anthropic and the timing just after Dario Amodei argued for tighter government oversight of frontier AI — leads to Duncan's question about access. If getting the best model starts to depend on proving your nationality, that is a structural risk for labs and buyers outside the United States.
Duncan asks what the shift means for non-US labs such as Canada's Cohere and France's Mistral, and points readers to The Prompt Factory for the fuller timeline and threads clustered around the main story.
To see how a cost-saving model switch actually fails in production, start here.
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It answers what breaks when a small model replaces a frontier model, and what it takes to make the swap work.
Duncan compares Claude Sonnet at two dollars per million input tokens with Nvidia's Nemotron 3.5 Lightning at ten cents, describing the gap as twenty times cheaper on input and forty times cheaper on output.
They moved one production agent to the small model, and it broke immediately: it fired one search and stopped, where it had previously worked through a dozen tool calls.
The traces showed almost every failure was mechanical.
The model called search instead of hybrid_search, a name that does not exist but blends names it had seen.
It passed query as a parameter to a tool that takes name.
It filled an optional date field with the literal string None instead of leaving it empty.
It typeset a citation with a non-breaking hyphen that looks identical to a normal one and quietly failed a pattern match in UI code.
After two failed designs, Duncan built a harness that expected the model to be almost right and handled it gracefully.
They also describe a week when an output sanitiser sat silently dead after a library upgrade renamed a field.
The cost point is tied to energy: a fraction of the cost means a similar fraction of the power.
For brands planning a campaign
For a technical content collaboration on agent harness engineering and cost-efficient model selection, this is a concrete production case, not a model benchmark. It supports a piece or talk on defensive programming, tool schemas, model routing and energy trade-offs, with exact failure examples a developer audience can test.
Start here to understand why open-weight models running on ordinary hardware matter to agent builders and the compute debate.
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It answers whether serious agentic work really requires enormous data-centre compute.
Duncan calls Artificial Analysis the closest thing to an independent referee for LLMs, pointing to its agentic leaderboard for tool use, planning and multi-step problem solving.
Qwen3.8-27B sits at number seven, runs on their laptop, uses an Apache 2.0 licence and needs no API key or data centre.
They translate the comparison for a CFO: the Qwen model is over a quarter of the input cost and an eighth of the output cost of Claude Opus.
They also note that Qwen comes from Alibaba and that Anthropic has accused Alibaba of using Anthropic model outputs to help train Qwen, adding that there is no way to know how true this is.
Setting that aside, they argue that if Alibaba can make a model this good that runs on a laptop, others can too, and that the data-centre and energy constraints in the press may not be as fixed as they appear.
For brands planning a campaign
This is a strong starting point for campaign content on open-weight models, local inference and compute or energy economics. Treat the training-data allegation as an allegation, with clear sourcing, and use the cost and hardware comparison as the practical hook.
Read this to see how a small technical idea moves from one person's routing layer to an open-source ecosystem in days, and what remains unresolved.
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It explains what a decision model is good for and where the hard parts are.
Duncan dropped a Jev decision model into a chatbot's routing layer, found it faster and cheaper, and said it had not got a decision wrong yet.
Three days later, the story was the copying.
A developer on r/LocalLLaMA pointed out that they had published essentially the same architecture with open weights in March last year.
Someone shipped openjev on Hugging Face.
Jared Palmer at Vercel released Kev-0.5B, a Jev-compatible decision model that trains and runs on a laptop.
Google's Gemma team showed DiffusionGemma doing the same trick.
Harrison Chase at LangChain said Jev had sparked more internal demos than any launch they could remember, including anything from OpenAI or Anthropic.
Duncan says the excitement has not yet addressed the harder problems, especially calibration and tooling.
A decision model is only useful if its 80% confidence actually means 80%.
They also point to The Prompt Factory coverage, including a toy language a Cloudflare engineer built where if-statements are model judgements.
For brands planning a campaign
For an AI infrastructure audience, this supports content on routing layers, decision models, open implementations and the calibration gap between a demo and a dependable system. It offers a timely angle without overclaiming that decision models are production-ready.
Start here to follow how access to a leading model can turn into a government and investor story.
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It explains what a takedown means for non-US labs and anyone choosing a model provider.
Duncan follows up on the Anthropic Fable 5 takedown with reporting on how the order came about.
Amazon's CEO reportedly flagged a jailbreak to officials, setting the shutdown in motion.
Amazon is also one of Anthropic's largest investors, an interesting twist.
The order arrived just after Dario Amodei had been making the case for tighter government oversight of frontier AI.
Duncan asks what matters for everyone else: will reaching for the best model start to depend on proving your nationality, and what does that mean for non-US labs such as Canada's Cohere and France's Mistral?
They say the full timeline and related threads are in that week's edition of The Prompt Factory newsletter.
For brands planning a campaign
This is useful for policy, geopolitics and model-access content aimed at AI infrastructure buyers. Keep reported elements clearly attributed and separate from Duncan's analysis of the consequences for non-US labs.
Read this to understand how Duncan edits AI news for busy readers and turns analysis into a test someone can run.
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It shows what makes an AI newsletter worth opening when there is more news than anyone can use.
Duncan cut The Prompt Factory newsletter to about half the length of the previous week's and says it is better for it.
The format favours analysis over news: three things that mattered most, one line of links for anything else worth a look, and one thing to actually do.
They say picking three is harder than picking six because it forces a view.
That week's view: renting AI got dearer and owning it got easier.
OpenAI halved the included usage on its $200 plan and Google cut its free tier, while open decision models got close enough to the hosted versions to run on a laptop.
Barnacle Labs released Cyprid, an open-source way to try them on a Mac, the same week.
The action: run a test in an evening.
The newsletter lands on Sundays.
For brands planning a campaign
This suits a sponsored or co-produced newsletter built around one practical test rather than a news roundup. It also shows an editorial approach a B2B AI infrastructure partner can work with: a few argued points, an open-model cost comparison and one action for the reader.
Agent harness engineering and tool-call failure modes
In a production experiment, Duncan Anderson moved an agent to Nvidia's Nemotron 3.5 Lightning because it cost much less than Claude Sonnet, then traced failures where the small model fired one search and stopped.
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The failures were mechanical: wrong tool names, wrong parameters, a literal "None" in an optional date field, and a non-breaking hyphen breaking a citation pattern.
The response was defensive programming: a harness designed for a model that is "almost right", after two earlier designs failed.
A later library upgrade renamed a field and silently killed an output sanitiser, which is presented as a warning about integration upkeep.
For brands planning a campaign
A technical collaboration can focus on harness design, tool schemas, trace review, and defensive parsing. The post offers a case study for vendors selling agent reliability, observability, or model-routing tools.
Duncan Anderson compares token prices and operational trade-offs between frontier and smaller models, arguing that cost tracks compute and compute tracks energy.
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The agent experiment is framed as both a cost decision and an energy decision, with the claim that a fraction of the cost means a similar fraction of power. The practical focus is whether a cheaper model can sustain multi-step tool use once the harness handles near-miss outputs—not just its sticker price.
For brands planning a campaign
A campaign can connect model-routing economics to sustainability or infrastructure efficiency. Concrete numbers and a production failure story make the topic easier to discuss than abstract cost claims.
Duncan Anderson uses Artificial Analysis and its agentic leaderboard as a reference point, highlighting Qwen3.8-27B at number seven while running on a laptop.
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The post notes Apache 2.0 licensing, open weights, no API key, and cost ratios against Claude Opus. It also acknowledges an Anthropic accusation that Alibaba used Anthropic outputs to train Qwen, says there is no way to know how true this is, and argues that strong agentic work does not necessarily require enormous compute.
Uses benchmark leaderboards and CFO-style cost comparisons.
The training-data accusation is explicitly unverified.
For brands planning a campaign
A brand in local inference, open-weight infrastructure, or edge AI can use the post to discuss where small models are now credible and what operational constraints remain. Ask for sourcing before repeating training-data claims.
Duncan Anderson describes dropping a "Jev" AI decision model into a chatbot routing layer and watching the idea spread quickly: a similar open-weight architecture from r/LocalLLaMA, openjev on Hugging Face, Kev-0.5B from Jared Palmer at Vercel, DiffusionGemma from Google's Gemma team, and Harrison Chase's comment that Jev sparked more internal demos than recent major launches.
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The post distinguishes the excitement from unresolved work on calibration and tooling: a decision model is only useful if 80% confidence actually means 80%.
Tracks public replications and expert reactions to an emerging format.
Calibration and tooling remain unresolved.
For brands planning a campaign
A collaboration can examine decision-model routing, calibration, and evaluation. The post suits a brand that wants to discuss fast-moving open-source adoption without treating every replication as production-ready.
Duncan Anderson follows the Anthropic Fable 5 takedown as a policy story: Amazon's CEO reportedly flagged a jailbreak, Amazon is also one of Anthropic's largest investors, and the order arrived just after Dario Amodei argued for tighter government oversight.
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The post asks whether access to the best model will depend on proving nationality, and what that means for non-US labs such as Canada's Cohere and France's Mistral. It points to The Prompt Factory for a fuller timeline.
For brands planning a campaign
A brand working on AI governance, cross-border access, or enterprise risk can use the topic to discuss dependence on US-hosted frontier models. The post is analysis, not a neutral news roundup; frame a collaboration accordingly.
Newsletter format built around analysis and one action
Duncan Anderson describes The Prompt Factory's shift to a shorter format: three things that mattered most, one line of links, and one thing to actually do.
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The stated aim is more analysis than news. The sample view is that renting AI got more expensive while owning it got easier, citing OpenAI halving included usage on its $200 plan, Google cutting its free tier, and open decision models getting close enough to run on a laptop, alongside Barnacle Labs' open-source Cyprid for trying them on a Mac.
Structures the newsletter around curation, analysis, and one action.
For brands planning a campaign
A brand can evaluate The Prompt Factory as a recurring analysis slot with a practical action, rather than a headline feed. It suits a sponsored or partner edition that needs a sharp view and one executable test.
Duncan S Anderson writes and edits The Prompt Factory newsletter, and discusses model vendors whose products they test, price out, and run in their own agent work.
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The posts describe Claude, Nvidia's Nemotron, and Alibaba's Qwen models being used or evaluated in production agents. Before a campaign, ask them to clarify who owns the "production agents" and "our chatbot" they refer to, and disclose any sponsorship, affiliate, client, or investment relationships.
The Prompt Factory
Owned business
Anderson writes and edits The Prompt Factory, a weekly newsletter.
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They describe changing its format so each edition carries the three stories they think mattered most, one line of links, and one action to take. They have always wanted it to be more analysis than news, and used one week's edition to write up the decision-model story.
Claude models appear throughout the posts as the paid baseline Anderson compares cheaper options against.
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They quote Claude Sonnet's input cost at two dollars per million tokens and say Claude Opus costs more on both input and output than the Qwen model they run locally. A separate post discusses the shutdown order on Anthropic's Fable 5 model and notes Anthropic's accusation that Alibaba trained Qwen on Anthropic model outputs.
Anderson moved one of their production agents onto Nvidia's Nemotron 3.5 Lightning, which they price at ten cents per million input tokens against Claude Sonnet's two dollars.
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They describe the failures and the harness they built to keep the agent running on it.
Anderson highlights Alibaba's open-weight Qwen3.8-27B, which they run on their own laptop under an Apache 2.0 licence with no API key, as a sign that capable agentic models no longer require data-centre compute.
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They also report Anthropic's accusation that Alibaba used Anthropic model outputs in training Qwen and say there is no way to know how true it is.
Duncan S Anderson is best explored through a few practical threads.
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Start with the production-agent post on moving to a cheaper model and building a harness for near-miss tool calls, then read the companion argument that lower cost also means lower energy use.
The open-weight model post makes a laptop-scale benchmark claim; the decision-model post examines the gap between fast open replication and unsolved calibration.
The Prompt Factory newsletter shows the recurring format: a few argued points, a short line of links and one action a reader can run.
Follow the policy posts if provider access and cross-border risk matter to your decisions.
Both examine why production AI agents fail after the demo stage, arguing that the surrounding harness or process architecture matters more than the model itself.
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Concrete deployment examples make the case.
Alex Cinovoj makes broad, high-volume claims in bold listicles and rants aimed at teams stuck between demos and production, while Duncan S Anderson offers measured analysis of trace-level failure modes and cost and energy trade-offs.
Both treat agent orchestration as the layer that determines enterprise adoption, covering permissions, observability, audit trails and the risk of invisible vendor lock-in.
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Jim Kaskade takes up the same themes at CEO and policymaker level, through geopolitical and procurement arguments, while Duncan S Anderson grounds their analysis in hands-on production experiments and token-cost economics.
Both track fast-moving AI and security developments for a professional audience, connecting model releases to operational risk.
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Mike Miller pairs a weekly news roundup with a sponsored vendor post on vulnerability remediation, while Duncan S Anderson offers argued analysis with one action, focused on agent engineering and model economics.
Consider Duncan S Anderson for a campaign aimed at a technically literate AI-infrastructure audience interested in what models cost to run and how agent systems behave in production—not AI as a business-trend story.
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The five posts read as written for people who build or budget for these systems: they compare input and output token prices with hardware and energy use, describe a production agent that broke after being moved to a cheaper small model and the harness built to fix it, and argue from an agentic leaderboard that a 27-billion-parameter model running on a laptop is close enough to hosted frontier models for some work.
The posts describe the recurring newsletter, The Prompt Factory, as a Sunday edition that picks three things that mattered, adds one action item, and deliberately favours analysis over news volume.
That combination points to readers who expect specifics and trade-offs, making the profile a fit for a collaboration on agent harness engineering and cost-efficient model selection.
The only audience figure published in the posts is a LinkedIn follower count; they provide no engagement, demographic, geographic, or professional data.
The posts indicate the content’s subject and intended readership, but do not establish who actually follows or reads it, or their employer, occupation, job function, or seniority.
Ask for recent platform analytics covering impressions, engagement rate, and follower demographics for posts comparable in topic, depth, and format to the proposed collaboration.
Ask whether they have audience survey results or another documented source for professional attributes such as role and seniority.
If not, plan on thematic fit alone and set expectations accordingly.
LinkedIn followers
4,004
LinkedIn account record for the listed profile · 2026-10-08
No sponsorship formats, media kit, rate card, or official partnership inquiry route appear anywhere in the posts here, so this section documents an absence rather than a menu of options.
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What the posts here do show is how Anderson publishes: the weekly Sunday newsletter The Prompt Factory, promoted through LinkedIn posts that link out to the full edition.
The current newsletter format presents three main stories, one line of links to everything else worth reading, and one suggested action, with the stated aim of being more analysis than news rather than a news digest.
For a buyer, the practical consequence is that any commercial arrangement would have to be opened directly through the LinkedIn account listed in the identity section.
There is no published specification of placement, sponsorship labelling, pricing, lead time, exclusivity, or review process to plan against, and nothing in these posts shows how a partner would be credited or disclosed.
Because these posts contain no sponsored content, affiliate disclosure, or partner credit of any kind, none of this should be read as evidence that Anderson does or does not accept sponsorships.
It means only that no offer is documented in these posts.
Confirming whether a partnership is possible, and on what terms, requires a source beyond these posts: a media kit, a sponsorship or contact page, or an explicit statement from the creator.
Good match, with commercial terms unconfirmed. The proposed collaboration is the subject of the strongest evidence in this profile, and the writing targets the people who would attend or read it.
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Brief
A B2B AI infrastructure company wants to assess Duncan S Anderson for a technical content collaboration on agent harness engineering and cost-efficient model selection.
Reasons
The production case offers specific material for content. In one post, they describe moving a live agent from Claude Sonnet at two dollars per million input tokens to Nvidia's Nemotron 3.5 Lightning at ten cents—twenty times cheaper on input and forty times on output. The agent broke immediately, firing a single search instead of working through a dozen tool calls. Trace review showed nearly every failure was mechanical: a call to search instead of hybrid_search, query passed where the tool expected name, the literal string "None" written into an optional date field, and a non-breaking hyphen in a citation that broke a pattern match in UI code.
The remedy is squarely on the buyer's topic. After two failed designs, they rebuilt the harness to expect a model that is almost right. They also describe a separate incident in which an output sanitiser sat silently dead for a week after a library upgrade renamed a field. For a vendor selling reliability, observability, or routing, this is first-hand material rather than a benchmark claim.
Cost selection is framed for both engineering and finance readers. The same post links cheaper tokens to energy, arguing that a fraction of the cost means a similar fraction of the power. A second post translates an open-weight comparison into figures a CFO would use: the Qwen model at over a quarter of the input cost and an eighth of the output cost of Claude Opus, running on a laptop under an Apache 2.0 licence with no API key.
Their newsletter offers a workable collaboration format. The Prompt Factory is described as three things that mattered, one line of links, and one thing to actually do—deliberately more analysis than news. They say picking three is harder than picking six because it forces a view. A partner seeking a sharp position and one executable test can work with that structure.
The content points to an audience of people who build or budget for AI systems, rather than readers following AI as a business-trend story.
Cautions
No sponsorship format, rate card, media kit, or inquiry route is published. Agree terms, placement, labelling, lead time, and exclusivity directly through LinkedIn; confirm these arrangements rather than assuming they exist.
The posts show a LinkedIn follower count of 4,004, observed on 8 October 2026, but no engagement, demographic, geographic, or seniority data. Assess the fit thematically and ask for platform analytics before setting reach targets.
The posts document no commercial disclosure practice: they show no sponsored post, affiliate label, or partner credit. Agree on disclosure from the start rather than assuming an established approach.
When to choose a specialist
Weak match. The reviewed work is written analysis for people who build or buy AI systems. It offers no evidence of consumer reach, video output, or the audience data this campaign depends on.
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Brief
A consumer AI app's growth team wants a paid creator to drive installs among a general consumer audience through short-form video.
Reasons
The subject matter is technical throughout: an agent harness and its tool-call failure modes, token price ratios, an open-weight model running on a laptop, a decision model dropped into a chatbot routing layer, and a model takedown treated as a policy story. None supports a lifestyle or general-consumer pitch.
The only recurring publication identified is The Prompt Factory, a Sunday newsletter. The format change described is toward fewer items and more argument. That depth-first format is a poor vehicle for install-driven reach content.
The posts show no video, audio, visual, or short-form work. Ask for examples before planning around that capability.
The audience evidence cannot support a consumer campaign. It consists of a follower count on one platform, with no engagement rate, demographics, geography, or survey data. The audience section also says job function, seniority, and employer are unverified.
No sponsorship offer, rate information, or inquiry route is documented. Ask for the offer and pricing before trying to schedule this paid creator campaign.
Cautions
The reviewed material offers no evidence of a consumer or video brief; it does not rule one out.
If the growth team's real need is credibility with technical buyers, that is a different brief and closer to the good-fit case above.
A worked campaign example
Angle
A sponsored edition of The Prompt Factory built around one reproducible test, supported by one longer technical piece. The newsletter edition keeps its established shape: three things that mattered, one line of links, and one thing to actually do. The hook is an action a developer can run in an evening: take a cheap small model, wire it to a realistic tool suite, and watch it reproduce the four mechanical failure classes that show up in traces, then add handling for each. The longer piece presents the same material as a method rather than a war story, covering the trace-review habit, defensive-parsing decisions, and the library-upgrade outage that took a sanitiser offline without anyone noticing.
Deliverables
One newsletter edition in the three-things, one-line, one-action structure, with a reproducible harness test as the action and three items drawn from model routing, tool schemas, and the open-weight cost picture.
One companion technical piece or recorded session covering trace review, defensive parsing for near-miss tool calls, and monitoring for silent integration breakage after dependency upgrades.
A short reader checklist: tool-name verification, parameter schema checks, optional-field handling, citation and string normalisation, and a post-upgrade smoke test for output sanitisation.
Open items
Agree terms, placement, labelling, lead time, and exclusivity directly with Duncan S Anderson through LinkedIn; no media kit, rate card, or sponsorship inquiry route is documented.
Ask for platform analytics on impressions, engagement, and follower demographics for posts comparable in depth to the proposed collaboration, plus any audience survey covering role and seniority.
If the campaign covers open-weight model training claims, keep the Anthropic allegation about Alibaba and Qwen clearly framed as an allegation. The post says there is no way to know how true it is; preserve that framing in any partner content.
Attribute the cost-to-energy argument to Duncan S Anderson as their own framing, not as a settled industry fact.
Reader task
Have a working developer finish the action and reproduce at least the wrong-tool-name, wrong-parameter, and literal "None" failures on a small model, then show one harness change that stops each. Run this test before agreeing to the collaboration.
Why it fits
The angle draws on the creator's own production experience, not a vendor script. The failure examples are concrete and testable, while the cost-and-energy framing gives finance readers a reason to keep reading past the engineering detail.
Pick Alex Cinovoj when the collaboration needs a failure-and-repair case study rather than a comparative analysis.
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The two posts lead with shipped-agent counts and named incidents: a Series B fintech that spent $120K over six months and ended up firing 847 API calls instead of 12, with a $6,200 first-week bill, rebuilt in three weeks at $0.08 per workflow; a Sydney logistics firm at $180K; and a Dutch firm claiming 60% admin reduction.
Cinovoj also states the buyer's argument directly: the model is roughly 10% of what makes an agent work, while tools, rules, sandboxes, guardrails, and observability are the rest.
For a vendor pitching reliability and observability, that is campaign-ready framing delivered as a story, faster to commission than Duncan S Anderson's trace-by-trace method.
Alex is the stronger choice for a provocative narrative post or a worked example drawn from client engagements, and the weaker choice when the deliverable must be a reproducible test a reader can run in an evening.
The trade-off is rigor versus reach mechanics.
These posts are built for engagement: very short lines, a closing question inviting horror stories, and repost and follow prompts.
The numbers are asserted with no method, link, or named client, so they cannot support a vendor's reliability claims without independent checking.
There is also no recurring analytical publication or portable exercise in these posts, only persuasion, so a campaign aimed at readers looking for a checklist or a testable harness change lands better with Anderson.
Pick Jim Kaskade when the collaboration targets executives rather than working developers.
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The posts argue that the agent, not the model, becomes the control plane for work, and that whoever owns agent routing eventually owns procurement because invisible lock-in replaces human comparison of alternatives.
The other post examines military AI procurement: alignment becoming procurement requirements and audit trails, and the blunt position that the safest military AI is open evaluation and enforceable constraints rather than refusal.
That framing suits a vendor selling governance, permissions, and observability to budget holders, and reaches policy and CEO readers that a trace-level engineering post does not.
Choose this for a campaign on enterprise orchestration or procurement accountability.
This misses the implementation layer a campaign promising developer-ready guidance depends on.
These posts include no traces, tool schemas, or cost arithmetic, so they do not demonstrate cost-efficient model selection.
The strongest content is an argued position about where the market is heading, plus a call to action pointing to CEO playbooks, not first-hand engineering.
If the collaboration promises developers a reproducible test, this carrier is weaker than Duncan S Anderson's harness material.
Pick Mike Miller when the campaign fits security practitioner coverage better than AI engineering.
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The roundup collects dated developments, including a CVSS 9.5 Citrix Netscaler issue with tens of thousands of exposed instances, an OpenAI training-run safety process, and a new cyber benchmark index.
It gives a sponsor a slot in a recurring news cadence aimed at security teams.
The second post moves from vulnerability volume to remediation, showing how vibe-coded applications multiply what teams must triage and walking through an agent remediation flow: understand the issue or dependency, find the safest upgrade, apply the fix, verify it, and open a pull request, with Patch Tuesday recorded at nearly 1,000 vulnerabilities.
That post states a partnership with Legit Security and is written as vendor-facing content, establishing precedent for sponsored placement and a format to fit.
Use it for a campaign on security remediation, CVE triage, or a named sponsored slot in an existing publication rhythm.
The practical detail is a supplier's process retold in summary, while the roundup aggregates rather than tests.
Neither post shows the writer running an agent and reading the traces.
Almost nothing addresses harness engineering or price-per-token model selection, the buyer's stated subject, making this the most adjacent of the three rather than a close substitute.
Ask how a sponsored placement is labelled and measured.
Duncan S Anderson's five complete LinkedIn posts cover a production agent moved onto a cheaper model and the tool-call failures found in its traces, token cost and power use, an open-weight model running on a laptop, decision models and their rapid open replication, a model takedown treated as a policy story, and the format of The Prompt Factory newsletter.
The posts have no publication dates, so their order and timeframe could not be established, and no position here is tied to a date.
The only audience figure published here is a LinkedIn follower count of 4,004 observed on 8 October 2026; no engagement, demographic, or geographic data is published here.
The full write-ups and newsletter editions referenced inside the posts do not appear in the posts, which also do not establish a biography, earlier career, books, talks, or other signature work.
Claims by other people that appear in the posts, such as the replication of the decision-model idea by other developers and the training-data allegation against Alibaba, are reported as their claims rather than Duncan's own.
The posts do not state any sponsorship, affiliate, client, or investment relationship, and do not name the business behind the "production agents" and "our chatbot" referred to in the posts.
