Anapoly AI Labs

Log in

Author: Alec Fearon

  • Use cases for NotebookLM

    Posting in his SubStack Adjacent Possible, Steven Johnson discusses how “language models are opening new avenues for inquiry in historical research and writing“. He suggests they can act as collaborative tools, rather than replacements for the writer’s engagement with primary sources.

    Johnson argues that NotebookLM is designed to facilitate rather than replace the reading of original sources. I t does so by making the entire source readable within the app, and by provinding inline citations linked directly to the original material.

    He identifies some interesting use cases.

    The AI can be a tool for collaborative brainstorming by allowing users to explore different hypotheses and see patterns within personally curated sources.

    NotebookLM can be used for targeted information retrieval.

    • It can help “fill in blank spots” or remind users of forgotten details from their readings.
    • The tool is valuable for fact-checking against uploaded source material.
    • For specific information, like in a car manual, it can provide direct answers to questions through a conversational Q&A format.

    It can enhance serendipitous discovery by suggesting surprising, less obvious connections amongst the sources.

    It can create mind maps from the sources, in effect indexing them on the fly.

    Finally he speculates on a future where e-books could come with a NotebookLM-like interface. This would bundle together the main work with all the original sources used by the author, enabling “timelines, mind maps, and explanations of key themes, anything you can think to ask”.

  • How ChatGPT helped draft our first acclimatisation lab setup

    Date: 24 June 2025

    transparency label: AI-heavy

    Our latest Lab Note records a quick experiment where I asked two ChatGPT models to draft the outline for an “acclimatisation” session – the starter lab we plan to run with newcomers to AI.

    Highlights:

    • Model face‑off: I ran the same prompt in parallel on model o3 and model 4o. The reasoning‑focused o3 delivered a tight nine‑part outline. 4o wandered off‑piste.
    • Time cost: The branch test took three minutes and gave us a clear winner.
    • Transparency: The Lab Note carries an AI‑heavy label because most of the prose came straight from o3. I trimmed, corrected one hallucination, and signed off.

    If you are curious about our process or want to see how structured prompting keeps the bot on track, read the full note here: First Acclimatisation Session Lab Note →

  • Drafting Anapoly’s first Lab Setup with ChatGPT

    Transparency label: AI‑heavy (ChatGPT model o3 produced primary content; Alec Fearon curated and lightly edited)

    Purpose of experiment

    Use ChatGPT as a thinking partner to create a first draft of an acclimatisation lab setup for Anapoly AI Labs and to compare output quality between two models (o3 and 4o).

    Author and date

    Alec Fearon, 24 June 2025

    Participants

    • Alec Fearon – experiment lead
    • ChatGPT model o3 – reasoning model
    • ChatGPT model 4o – comparison model

    Lab configuration and setup

    Interaction took place entirely in the ChatGPT Document Workbench. The first prompt was duplicated into two branches, each tied to a specific model. All files needed for reference (conceptual framework, lab note structure) were pre‑uploaded in the project space.

    Preamble

    Alec wanted a concise, critical first draft to stimulate team discussion. The exercise also served as a live test of whether o3’s “reasoning” advantage produced materially better drafts than the newer 4o model.

    (more…)

  • No substitute for reading the paper

    transparency label: Human-only

    … what I can say is that a theme throughout this self-analysis is this: I find ChatGPT to be a really useful tool when I already have some idea of what I want to do and when I’m actually engaged with the issue. I find it much less reliable or useful for completely automating parts of the process. There’s no substitute for reading the paper.

    Source: Sean Trott in his newsletter How I use (and don’t use) ChatGPT on 24 June 2025

  • ChatGPT models: which to use when?

    transparency label: Human only

    ChatGPT-4ofast, for brainstorming, quick questions, general chat
    o3powerful, for serious work (analysis, writing, research, coding)
    o3-proultra-powerful, for the hardest problems

    Source: One Useful Thing, Substack newsletter by Ethan Mollick, 23 June 2025

  • That was the moment …

    transparency label: Human only

    It hit me that generative AI is the first kind of technology that can tell you how to use itself. You ask it what to do, and it explains the tool, the technique, the reasoning—it teaches you. And that flipped something for me. It stopped being a support tool and became more like a co-founder.

    A quote from BRXND Dispatch, a SubStack newsletter by Noah Brier, which featured Craig Hepburn, former Chief Digital Officer at Art Basel.

  • Lab Note: modelling our own use of AI tools

    Transparency label: AI-heavy

    Purpose of experiment

    To identify and configure an AI toolkit for Anapoly AI Labs that credibly models the use of general-purpose AI tools in a small consultancy setting.

    Author and date

    Alec Fearon, 24 June 2025

    Participants

    Alec Fearon, with Ray Holland and Dennis Silverwood in email consultation
    ChatGPT-4o

    Lab configuration and setup

    This setup models a real-world micro-consultancy with three collaborators. It assumes limited budget, modest technical support, and a practical orientation. We aim to reflect the toolkit choices we might recommend to participants in Anapoly AI Labs sessions.

    Preamble

    If Anapoly AI Labs is to be a credible venture, we believe it must model the behaviour it explores. That means our own internal work should demonstrate how small teams or sole traders might use AI tools in everyday tasks – writing, research, analysis, and communication – not just talk about it. This lab note outlines our proposed working configuration.

    Procedure

    We identified common functions we ourselves perform (and expect others will want to model), for example:

    • Writing, summarising, and critiquing text
    • Researching topics and checking facts
    • Extracting and organising information from documents
    • Sharing and collaborating on files
    • Managing project knowledge

    We then selected tools that:

    • Are available off the shelf
    • Require no specialist training
    • Are affordable on a small-business budget
    • Can be configured and used transparently

    Findings

    Core Tools Selected

    FunctionToolLicenceNotes
    Writing & promptingChatGPT Team£25–30/m/userMain workspace for drafting, reasoning, editing
    Search & fact-checkingPerplexity Pro$20/m/userFast, source-aware, good for validating facts
    Document interrogationNotebookLMFree (for now)Project libraries, good with PDFs and notes
    Office appsMS 365 or Google£5–15/m/userMatches common small business setups
    Visual inputsChatGPT VisionIncluded with ChatGPTUsed for images, scans, and screenshots

    Discussion of findings

    This configuration balances affordability, realism, and capability. We expect participants in Anapoly AI Labs to have similar access to these tools, or to be able to get it. By using these tools ourselves in Anapoly’s day-to-day running, we:

    • Gain first hand experience to share
    • Create reusable examples from real work
    • Expose gaps, workarounds, and lessons worth documenting

    We considered whether personal licences could be shared during lab sessions. Technically, they can’t: individual ChatGPT and Perplexity licences are for single-user use. While enforcement is unlikely, we’ve chosen to adopt the position that participants should bring their own their own AI tools – free or paid – to lab sessions as part of the learning experience. This avoids ambiguity about licencing and sets the ethical standard we want to maintain.

    Conclusions

    This toolkit would enable us to model our own small-business operations, treating Anapoly itself as one of the lab setups. That would reinforce our stance: we don’t claim to be AI experts; we’re practitioners asking the questions small businesses wish they had time to ask, and showing what happens when you do.

    Recommendations

    • Configure project workspaces in ChatGPT Team to reflect different lab contexts
    • Maintain prompt libraries and reasoning trails
    • Make costs, configurations, and limitations explicit in diary and lab notes
    • Evaluate whether to add AI-enhanced spreadsheet or knowledge tools (e.g. Notion, Obsidian) in future iterations

    Tags

    ai tools, toolkit, configuration, modelling, small business, chatgpt, perplexity, notebooklm, office software, credibility

    Glossary

    ChatGPT Team – OpenAI’s paid workspace version of ChatGPT, allowing collaboration, custom GPTs, and project folders.
    NotebookLM – A Google tool for working with uploaded documents using AI, currently free.
    Perplexity Pro – A subscription AI assistant known for showing sources.
    Vision input – The ability to upload images (photos, scans) and have the AI interpret them.

  • Mapping the territory: a conceptual framework for our labs

    Transparency label: AI-assisted

    As Anapoly AI Labs begins to take clearer shape, we’ve stepped back to ask: what exactly is a lab, and how should we think about the different types we’re running or planning?

    We now have an answer in the form of framework that describes what labs are for, how they vary, and what kind of value they generate. This will help us give lab participants a sense of where they are and what comes next in the process of understanding how to make use of general-purpose AI tools. The framework will help us design better labs. It will evolve in line with our thinking about all these things.

    The framework defines four key functions a lab can serve:

    • Acclimatisation – helping people get comfortable with AI tools
    • Experimentation – trying out tasks to see what works and what doesn’t
    • Proof of concept – asking whether AI could handle a specific challenge
    • Iteration – going back to improve on an earlier result

    It also distinguishes between domains (like consultancy or authorship) and contexts (like “a solo consultant writing a project bid”). Labs are set up to reflect domain + context.

    The framework defines a simple set of participant roles – observer, explorer, and facilitator – and outlines the kinds of outcomes we’re hoping for: confidence, insight, and learning.

    The full conceptual framework is here, and we’ll continue to refine it as our practice develops.

    This diary post is part of our public notebook. It helps document not just what we’ve tried, but how we’re thinking and rethinking as we go.

    Transparency Label Justification: This post was developed in dialogue with ChatGPT. Alec directed the content, structure, and tone, while ChatGPT contributed drafts, edits, and structural suggestions. All decisions about framing and language were reviewed and approved by Alec. This collaborative process fits the “AI-assisted” classification under the Anapoly Online transparency framework.

  • Assessing AI support for defining a value proposition

    transparency label: AI-assisted

    Purpose of Experiment

    To test whether a general-purpose AI (NotebookLM) can generate a credible, insightful articulation of Anapoly AI Labs’ value proposition when prompted appropriately.

    Author and Date

    Alec Fearon, 22 June 2025

    Participants

    Alec Fearon, Ray Holland, NotebookLM (AI), Perplexity (AI)

    Lab Configuration and Setup

    This experiment was run using NotebookLM, in a notebook containing background files on Anapoly AI Labs. To explore how we might play to the strengths of different AIs, a follow-up was performed using Perplexity with the NotebookLM output as input.

    Preamble

    Procedure

    NotebookM was given this prompt:

    “What is Anapoly AI Labs’ core value proposition and how does it differentiate itself?”

    NotebookLM’s response was then given to Perplexity, with this prompt:

    “How does Anapoly’s hands-on approach help me understand AI better in my work?”

    Findings

    NotebookLM responded with a detailed analysis of Anapoly’s strategic positioning:

    Anapoly AI Labs’ core value proposition lies in bridging the gap between AI curiosity and practical application for non-technical professionals and small businesses. They address the common issue where individuals are interested in AI but are unsure how to use it effectively in their daily work. Their purpose is to help these professionals explore how general-purpose AI can assist with everyday tasks, such as improving productivity, creating better quality output, or saving money.

    Anapoly AI Labs differentiates itself through a distinctive “modeling exploration” approach and its stance on credibility:

    • Hands-on, Practical Exploration over Theory or Expertise: Unlike traditional courses, product demos, or sales pitches, Anapoly AI Labs focuses on hands-on exploration of general-purpose AI tools in simulated real-world contexts. The emphasis is on demonstrating “what AI tools can and can’t do when pointed at everyday work,” rather than teaching complex AI theory or coding. This involves setting up specific “labs” for different market segments, such as a Small Consultancy Office or Independent Author, to apply AI to real problems.
    • Credibility Through Transparency and Shared Journey: A central aspect of their differentiation is their approach to credibility. They explicitly state they “don’t claim to be AI experts”. Instead, their credibility is built upon a “shared journey of discovery and documented practical insights”. As practitioners “in the early stages of building with the tools”, they aim to show that they are serious, curious, and hands-on. The AI itself helped refine this stance, suggesting that their value comes from “modeling exploration” and “asking the questions small businesses wish they had time to ask, and showing what happens when you do”. This is presented as more credible than a static CV or pre-existing expert status.
    • Public Documentation and Transparency: Anapoly AI Labs maintains an online presence called “Anapoly Online,” which functions as a “public notebook”. This platform is used to “document real tests, learning, missteps, and insights,” sharing what works and what doesn’t. It is designed to be “lean, understated, and quietly credible”, building a “body of work others can trust” bit by bit through openness and demonstrated effort. They also have a transparent system for disclosing AI involvement in all published material, using classifications like “Human-led,” “AI-assisted,” or “AI-heavy”.
    • Focus on the “Capability-Absorption Gap”: The labs directly address the “capability-absorption gap,” which refers to why businesses struggle to adopt AI despite its capabilities. By providing a low-barrier-to-entry, informal, and social environment for peer learning, they aim to make AI adoption more accessible and less confusing for non-technical users.

    Perplexity then provided the following simplified summary:

    “Anapoly’s hands-on approach helps you understand AI better in your work by letting you actually try out AI tools on real tasks that matter to you, instead of just reading about them or watching demos. This practical experience shows you what AI can and can’t do in your daily work, making it easier to see how it fits into your routine and where it can save you time or improve your results.

    You get to experiment in a safe, informal setting, learn from real examples, and see both successes and mistakes. This helps you build real confidence with AI, instead of just theory, and makes it much easier to spot where AI could help you most in your job.”

    Discussion of findings

    The NotebookLM output demonstrated that a general-purpose AI can synthesise a coherent and plausible value proposition from structured background material. Perplexity, by contrast, generated a shorter and more audience-friendly version in response to a focused, user-oriented question.

    Together, these outputs show complementary strengths: NotebookLM delivers a comprehensive internal formulation; Perplexity extracts an accessible, benefit-led summary. Used in tandem, these tools help refine messaging for different audiences: internal strategists vs. prospective participants.

    Of particular interest is NotebookLM’s identification of the “capability-absorption gap”, a concise and useful term for a key problem that Anapoly AI Labs addresses. While the founders had recognised this issue in practical terms, the AI’s phrasing sharpens it into a strategic talking point. Framing Anapoly’s purpose in terms of reducing this gap may prove valuable in both internal planning and external communication.

    This experiment also highlights the value of re-prompting and testing across different AI models to triangulate clarity and tone.

    Recommendations

    1. Use AI tools like NotebookLM to draft key positioning statements, especially when materials are already well developed.
    2. Always review AI-generated value propositions critically. Look for overfitting, vagueness, or unearned claims.
    3. Use simpler AI prompts with tools like Perplexity to test how propositions land with a non-specialist audience.
    4. Consider publishing selected AI outputs as-is, but with clear disclosure and context-setting.
    5. Repeat this exercise periodically to test whether the value proposition evolves or ossifies.

    Tags
    value, lab-setup, worked, use-cases, prompting, ai-only, positioning, credibility, communication, capability-absorption-gap

    Glossary

    • Modeling Exploration: A term used to describe Anapoly AI Labs’ approach—testing and demonstrating AI use in practical contexts without claiming expertise.
    • Capability-Absorption Gap: The space between what AI tools can do and what users actually manage to adopt in real settings. First coined (in this context) by NotebookLM.
    • Public Notebook: Anapoly Online’s role as a transparent log of what was tried, what worked, and what didn’t.
    • General-purpose AI Tools: Tools like ChatGPT or NotebookLM that are not tailored to a specific domain but can assist with a wide range of tasks.
    • AI-only: A transparency label denoting that the content was fully generated by AI without human rewriting or editorial shaping.
    • Overfitting: In this context, an AI response that sticks too closely to the language or structure of source material, potentially limiting originality or insight.
    • Vagueness: A tendency in AI outputs to use safe, abstract phrases that lack specificity or actionable detail.
    • Unearned Claims: Assertions made by AI that sound impressive but are not substantiated by evidence or experience in the given context.

    Transparency Label Justification. The experimental outputs (NotebookLM and Perplexity responses) were AI-generated and included unedited. However, the lab note itself – its framing, interpretation, and derived recommendations – was co-written by a human and ChatGPT in structured dialogue.
    ChatGPT’s role included: drafting the findings and recommendations, articulating the reasoning behind terms like “capability-absorption gap”, refining the explanatory framing, tags, and glossary.

  • Sandboxes

    transparency label: human-led

    The EU AI Act establishes a risk-based classification system for AI systems. Compliance requirements depend on the risk the system poses to users. Risk levels are set at unacceptable and high. General-Purpose AI systems like ChatGPT are not classified as high-risk but are subject to specific transparency requirements and must comply with EU copyright law. 

    Also, the Act “aims to support AI innovation and start-ups in Europe, allowing companies to develop and test general-purpose AI models before public release. That is why it requires that national authorities provide companies with a testing environment for AI that simulates conditions close to the real world. This will help small and medium-sized enterprises (SMEs) compete in the growing EU artificial intelligence market.“

    Unlike the EU, the UK has no general-purpose AI sandbox. The UK takes a sector-led approach to AI oversight, relying on existing regulators to operate their own sandbox initiatives under the government’s pro-innovation framework. Each sandbox is designed around the compliance needs and risk profiles of its domain. Existing sandboxes are focused on sector-specific or compliance-heavy contexts, for example:

    • FCA AI Sandbox (2025) – Financial services only; supports firms developing or integrating AI tools into fintech workflows.
    • ICO Regulatory Sandbox – Suitable for testing AI applications involving personal data, especially where GDPR-like safeguards are needed.
    • MHRA AI Airlock – For AI used in medical devices.

    These UK sandboxes are geared toward testing purpose-built AI tools or integrations into regulated industry IT systems. There is no current sandbox designed for SMEs exploring general-purpose AI like ChatGPT in everyday, low-risk tasks. While tools like ChatGPT could be included in sandbox trials (e.g. via the ICO for data privacy concerns or the FCA for financial services), these environments are not designed for routine or everyday use. They are structured for defined compliance challenges, not routine experimentation, and they require a defined project with specific compliance aims.

    A search by ChatGPT found no evidence of any current provision for open-ended, exploratory use of general-purpose AI by SMEs in unregulated or lightly regulated workflows. Anapoly AI Labs can occupy that space, modelling practical, credible AI use outside formal regulation.

    In that context, Anapoly’s modelling approach could include a cycle of experimentation followed by proof of concept (POC). A proof of concept is a small-scale test to check whether an idea works before investing in full implementation.

    Transparency Label Justification. This piece was authored by Alec Fearon. ChatGPT was used to assist with regulatory research (e.g. the EU AI Act and UK sandbox landscape), structure the argument, and refine language. The ideas, framing, and conclusions are Alec’s. AI contributed supporting detail and wording options, but did not generate or structure the content independently.