William Wong

Given the plethora of highly interesting ideas, projects, domains, and disciplines that our rich world is built of, how can one choose what to work on?

There is an argument in favour of long-term projects (missions): there are some affairs in the world today that seem to be solvable only with substantial persistence and patience. These issues, which are both limited to a single discipline but more frequently across multiple, seem to require considerable multi-year commitment before meaningful progress can be achieved.

Therefore: the question of what to work on flips: it's not only what one wants to work on but also what needs to be worked. Yet, one is unlikely to devote one's life to something merely because it is needed, without also finding it compelling. We are interested in the intersection.

How does one choose a mission if they decide they want to work on one? The answer is nontrivial because of the requirements:

(a) it has to be something that is needed in the world -- (usefulness dimension)
(b) it has to be something in which they have above-average competence (only insofar as the good outcome is realised) -- (aptitude dimension)
(c) it has to be something they want to work on -- (preference dimension)
(d) it has to be something they want to work on long-term — a successful mission becomes one’s life’s work -- (commitment dimension)

The difficulty of the answer lies in the inexact comparison of the degree of each of these dimensions per issue. For example, here's a list of issues:

1. Alzheimer’s
2. Economic stability of Western countries
3. Mental health of the young population

Let's reflect on each dimension.

(a) Usefulness: they are all extremely important; maybe one could claim economic stability is more important due to its impact to much more of the population of a country but that wouldn't be a convincing claim
(b) Competency: judging one's competency for each of these can be quite hard insofar as what skills are required for each problem/solution is genuinely unclear (even if a problem seems medical or economic, the practical path to the solution can require enterpreneurship or campaigning skills)
(c) Preference: people are interested in more than one disciplines. Helping people with medical problems can be fascinating and rewarding but so can scheming plans for eradicating homelessness.
(d) Commitment: all of these issues seem so distant and commitment a fairy tale unless one has already spent some time on them

I think this demonstrates the heart of the problem of choosing a mission. There are convincing arguments that each of the dimensions can be both very low and very high for someone. It's just very hard to say! So what do we do? I think our past experience is the best place to start.

1. Which country-level or global scale problem keeps coming back to your mind?

2. Is there a discipline or skill you feel you have a natural talent for?

3. What is more likely to work out depending on who you know and where you are? Try not to be biased when answering this. The point is the good outcome for the world! (For example, don't feel guilty if you are the child of the prime minister or something; whatever power you have, use it for the benefit of all)

4. What issue can really grab you? What do you see could be your life's work?

I feel these are good thinking paths to reflect and understand such a mission project. I've already been verbose but here are some more aspects:

• If one feels their reflections yield little insight maybe there is a lack of experience. So try new things! Experiment, do things you wouldn't normally do!
• It's worth thinking about one's mission intentionally. Find a weekend, clear your schedule, go on a walk, and just think about this — no other expectations other than spending time on thinking.
• Choosing is better than not choosing, even if the choice ends up being suboptimal. That's not to say one should rush a choice. Take a few years to think about this, experiment, try things out. But I would argue in favour of choosing a mission, eventually.
• I am not saying everyone should have a mission. Such an approach to a career/life is not for everyone. But I think it is for the people that are here at Newspeak House.

Last year I teamed up with Georgia Bullen, Executive Director of Superbloom Design, to offer the cohort a session on Funding 101 for Civic & Political Tech. We covered a lot of ground in just an hour, including candid takes on the funding landscape, entity and legal eligibility types, and winning strategies. Now I work for a foundation, so hopefully it'll be even better!

A casual, ask-the-expert session on how projects actually get funded in the real world with Georgia Bullen and Matt Stempeck.

​If you’ve ever thought “I have an idea… now what?”, this is for you.

​We’ll discuss the practical side of getting a project off the ground: where funding comes from, how to find opportunities, what early-stage teams should prioritize, and what makes an application stand out when you don’t yet have a track record.

​This session is especially relevant for fellow candidates thinking about what happens after a prototype: turning something scrappy into something sustainable.

​Topics we’ll likely touch on:

• ​Where to find funders and active opportunities

• ​What first-time projects should look for (and avoid)

• ​Legal structures and setup basics

• ​Signals of credibility when you’re unknown

• ​Partner development and early traction

• ​What strong grant applications actually look like

​Georgia is the Executive Director of Superbloom, where she works at the intersection of technology, movements, and public interest infrastructure, supporting teams building tools for civic participation, accountability, and collective action. Her work spans fundraising strategy, ecosystem building, and helping early-stage projects navigate the path from idea to impact.

​Matt Stempeck is a civic technologist and researcher focused on how technology can strengthen democracy and support real-world civic action. He is the founder of the Civic Tech Field Guide, a global collection of tools and organizations working in the public interest. He is faculty at Newspeak House (the London College of Political Technology) and has held roles across government, academia, and industry, including at Microsoft and Cornell Tech. His work focuses on helping projects move from ideas to impact, with an emphasis on practical strategies for participation, funding, and movement-building.

So it turns out, in government, civic society, and life more generally, finding out of if something actually works (eg, achieves the change you wanted it to) is actually rather hard. People have done excellent talks about this. The UK Government has got a book about this. And yet it's still all pretty hard, and people tend to be very confidently wrong.

I've spent a lot of my time trying to run evaluations in government, but it's not exactly a fun thing to teach... so instead, I'm proposing a bit of a lab experiment: let's run an experiment on ration club (or something else lab related you control!)

If people are interested, we'll spend a few hours talking through what sort of changes we might want to make to RC, then hopefully actually test them, robustly, record what changes, and run a full experiment from cradle to grave. We'll start from first principles, so a vague understanding of causal inference and statistics is very welcome, but absolutely not necessary.

PS: it will defo go wrong.

Relevant reading:

• all the evaluations linked above
• Some of the other topics of data + evidence
• Freakonomics!
• If you're feeling hardcore, Causal Inference for the Brave and True, my bible.

With the reach of generative AI, the term “slop” has risen in prominence. It represents a rejection of thoughtless content, often perceived to be low in quality or creativity.

Developers have always taken a view about the amount of effort they put in to a task - with a direct effect on the quality of code they deliver. This is a calculation based on deadlines, availability, resources, reputation, and plenty of other factors.

Opinion is strongly divided about AI-assisted coding. Some argue it supports increased productivity. Others cite poor quality outputs and unexpectedly high cost.

What does poor quality code look like?

I have been exploring the outputs from some generative AI coding tools. Some traits are pretty clear:

• Use of obscure and made-up terms

• Code mingled with narrative comments

• Spaghetti code

• Long or difficult to read functions

• Inflexible, hard-coded assumptions

• Dependencies on old libraries, difficult to upgrade

These issues are also referred to as technical debt.

Does it even matter?

Arguably, if code is only ever to be written by one person (or agent), then idiosyncrasies don't matter. Provided the developer knows where everything is, who cares if nobody else can figure it out? This may well be a naive position...

• Do you trust your coding agent to get everything right?
• What happens when you run out of credit?
• Do you want to be able to maintain it yourself?
• Are you sure nobody else will look at the code?

What can we do about it?

Over time, technical debt builds up in every project and the solution is to refactor the code - a sense-making process in which developers rewrite the offending parts. Solo developers and small teams working in isolation are particularly vulnerable to technical debt, but all teams experience it.

Running code reviews and writing code that any person can quickly understand is a good preventative measure. Some examples of established practices:

• Clear and meaningful variable names

• Use of well established design patterns

• Use of existing frameworks, rather than re-inventing the wheel

• DRY (Don’t Repeat Yourself)

• KISS (Keep It Simple Stupid)

• YAGNI (You Ain’t Gonna Need It)

• SOLID (principles for object oriented development)

• Separation of concerns (a precursor to the Single Responsibility Principle)

• Clean Code (and opinionated superset of principles and approaches)

How are coding agents any different?

Coding assistants work alone. They aren't trained to expect code review nor do they have code quality metrics. As they work, they develop personal shorthand and assumptions, which then shapes and styles the code they produce.

I have been collating a set of guidance, skills, and scripts for coding assistants, to raise the quality of their outputs. It's available here: instantiator/dev-qual

For this topic, we’ll discuss factors that contribute to code quality, and how they can be measured and applied - both to AI-assisted and human developed coding. With some metrics in hand, I’ll support you to compose rules for your language or framework of choice, or enhance those already found in dev-qual.

There are numerous opinions and camps around the ethics of various technologies (most strongly and recently, AI), which can create frictions even amongst political technologists who are aligned on other values - let alone people who do not consider themselves political technologists. I can provide advice for Fellows who wish to discuss or navigate polarised views around tech. I will not attempt to convert Fellows to any particular perspective, nor help them to do so with others. I will instead help them consider how they can locate their own work, projects, and views within the existing space; and engage with & prepare for potential criticisms and opposition.

My experience in this topic: Besides my broader work in tech accountability and safety, also at the NGO AlgorithmWatch I was responsible for a lot of work at the intersection of differering views on tech, and a spectrum of organisations who took different approaches and stances. I developed AlgorithmWatch's principles for use of Generative AI, building on surveys which showed diverging views of AI in our organisation. I was also responsible for our research strategy, much of which challenged technologies while also often using (and sometimes encouraged use of) the same technologies.

The problem of addressing false and misleading content online has a long history, but it became supercharged particularly after 2016. It has spawned much terminology - mis/disinformation, malinformation, FIMI, influence operations - and a range of countermeasures, from fact-checking to media literacy to regulations. New techniques - from fake accounts to deepfakes LLM poisoning - keep emerging, generating new countermeasures but also causing new panics.

The topic has become a major one in political technology discussions, and one which constantly generates new efforts and ideas - many of which do not engage well with existing discussions. There have also been changing views on the nature of the problem itself. Is the risk that minds will be changed in ways that swing elections, or cause mass violence, or even just sow distrust? Or targeted operations to deceive elites or radicalise vunerable individuals? Is the veracity or reliability of information really the best way to address online discourse? Should we be focused more on polarisation and radicalisation than deception - or are these all inextricably related anyway? Or is this problem all overblown anyway, with risks of bad solutions - some of which may threaten freedom of speech?

I can help fellows who wish to engage with "disinformation" navigate this history, and work on an informed basis through core questions: What do I think the problem is? Is my proposed solution meeting the right audiences? Has anyone else tried what I want to do? What might critique or resistance to my solution be?

Some material for starters:

https://www.oliver-marsh.com/sideways-looks--29--ai-pril-fools-also-dating-data/

"Why misinformation must not be ignored": https://pubmed.ncbi.nlm.nih.gov/39666498/

"In the age of AI, keep calm and vote on": https://www.politico.eu/article/artificial-intelligence-elections-ballots-vote-chinsinau-seattle-policymakers-technology/

Organising events in Newspeak Hall is one of the great perks of studying at Newspeak House; a fully equipped event space that holds up to ~100 people. It's hosted thousands of events for the college, everything from meetups to conferences, unconferences, dinners, hackathons, parties, exhibitions, lectures, co-working, debates, book launches, concerts, world cafes, fishbowls, screenings, workshops, and roundtables.

If you're interested in designing something together, I am happy to offer advice and support with event design, facilitation, logistics, catering, promotion, community strategy, and almost anything else you might need. Don’t worry if you don’t have a very clear idea of what you’d like to do yet - we can figure that out together.

I also manage the diary of the hall; it's a busy programme of community events, private hires, and events organised by fellows and faculty. As Fellowship Candidates, you will book dates through your agent. You might not be able to secure a specific exact date, but usually we can find a slot. Typically single day events are booked around a month in advance.

Finally, events should always be considered as part of a wider community strategy. I've written a short guide on how I typically approach this, though community-building is a fast-moving field with many open questions...

A lot of political and organisational problems are not really persuasion problems, they're legitimacy problems. People often know what needs to happen but disagree on who gets to decide, whether the process was fair, or whether they trust the people leading it. I'm interested in how legitimacy gets constructed in practice: through participation, transparency, shared ownership, evidence and creating processes people recognise as fair. Following Martin Conway, I treat legitimacy as historically contingent, socially constructed and contested — the interesting question is never "is this legitimate?" but "how did it become legitimate, and why then?"

For example, the covid memorial was not legitimate but it was made so. This was done by making sure the first volunteers wore flouresent jackets, had a sign put up and by the afternoon multiple front bench politicians had visited it. Then the council accepted it and paid for its upkeep as a historic memorial. Its legitimacy was constructed. Technically everyone could have been arrested for criminal damage.

Another example in the #Honourtheoffer campaign which was crearting legitimacy through organising information. When Oxford alumni felt colleges weren't honouring their access commitments, the concern was fragmented across dozens of colleges with decentralised decision-making, which meant if one fell, they all would. A hashtag gave people shared language and over 8,000 signed the petition, but the pivotal move was mundane: a public spreadsheet listing every college against its commitments, updated openly and pitting them against each other. The spreadsheet didn't just record reality, it organised it and whoever structures the information becomes trusted to interpret it. League tables, polling and Wikipedia all work this way.

Across my work I’ve repeatedly operated in environments where authority is fragmented such as campaigns, parties, community groups, MPs’ offices, networks and coalitions. I can help fellows think about questions like: How do you make decisions people will actually follow? How do you create buy-in without formal power? How do you design participation that is meaningful rather than performative? How do you create legitimacy for new technologies or organisational change?

• Martin Conway https://www.history.ox.ac.uk/publication/213503/manual

A model might be an equation, a map, a simulation, a story or a physical arrangement of stones. A Zen garden is not a landscape, yet it preserves or evokes something important about one. Metaphors work similarly: describing a career as a ladder or life as a journey creates an implicit model with particular structures, directions and possibilities. What happens when we make those assumptions explicit?

This topic weaves around the two core topics, Systems Thinking: Understanding Political Dynamics and Building Evidence: Data Science in the Age of AI. Those topics introduce ways to model systems and build evidence from data; this one turns those methods back on themselves. What assumptions are built into a model? What does it make visible, and what becomes harder to see? The aim is not to retreat from systems thinking or empirical evidence, but to use them more imaginatively and self-consciously.

Play gives us a way to explore and test models without treating them as settled truths. Roger Caillois distinguished competition, chance, simulation and vertigo, alongside a spectrum from spontaneous play to formal games. We can compete models against one another, introduce chance, inhabit them through role-play or deliberately destabilise their categories. Play both creates temporary systems of rules and allows us to bend, replace or step outside them.

I have spent much of my career constructing mathematical and computational models, but models need not begin with mathematics. We would build models of ourselves using metaphors, diagrams, objects, stories or games, then allow other people to interpret and disrupt them. The aim is to become more imaginative and self-conscious model-makers, better able to see what any representation reveals, assumes and excludes.

Core reading

• Roger Caillois, Man, Play and Games (University of Illinois Press, 2001).
• George Lakoff and Mark Johnson, Metaphors We Live By (University of Chicago Press, 1980).
• Ezequiel Di Paolo et al. "Simulation Models as Opaque Thought Experiments" (2000).

There is a new category of software, coming now, after agents harnesses the multiplayer harnesses!

(It's always like that with games, isn't it? First they release single-player and then people want multi-player! Pokemon on Gameboy, GTA, Elder Scrolls, etc.)

How does it work? Visualise this: you are part of team that works on a project (either software project or non-software). You want, while inside Claude Code, to mention a teammate to ask them something, let them know about something, or anything else. How do you do it? You can't! Agent harnesses like Claude Code and ChatGPT Codex do not have such a feature.

Actually, Anthropic does offer a version of this inside Slack (they call it Claude Tag)

But in any case, people are building their own open source self-hosted versions of such tools:

• Centaur.run (lives inside slack like Claude Tag)
• QM (either Slack and on the web)
• Windmill (designed around vibe-coded apps rather than a chat-driven harness)
• Cloudflare OS (built on top of Cloudflare Workers but could be adjusted to work self-hosted with celld)

This is a rapidly progressing category of software! Things will probably be very different in 3 months.

This topic builds on both Building Evidence: Data Science in the Age of AI and Listening to Politics at Scale. It applies the same questions about representation, sampling, classification and inference to an especially difficult and politically contested problem. Misinformation is not a single observable property of a post. An algorithm might examine language, compare claims with evidence, score the history of a source, trace patterns of diffusion or look for coordinated behaviour. Each method detects something different, and none gives us a simple mechanical test for truth.

The difficult cases expose the politics inside the technical problem. Who defines the ground truth? Is a sincerely mistaken claim the same as deliberate deception? How should a system handle satire, contested interpretations or knowledge that is still changing? A detector can also become a political instrument if its errors fall unevenly or if nobody can challenge its decisions.

I have worked on research involving bots, online communities and the spread of contested information. I can help you compare content-, source-, bot- and coordination-based methods, then design and test a practical detection process on difficult cases. The aim is to understand what a detector is really detecting, evaluate its errors and identify where human judgement and political power enter the system.

Core reading

• Francesco Pierri et al., “One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study” (2023).
• Salvador Lopez-Joya et al., “Dissecting a Social Bot Powered by Generative AI: Anatomy, New Trends and Challenges” (2025).

Using AI to build evidence can easily produce a house of cards. Give it some data and it can quickly return code, categories, charts, networks and a confident conclusion, with each layer resting on choices we may never have examined. That is a good reason to look underneath—but also an invitation to play. AI gives us a fast way to try methods, change assumptions and see what happens when we pull out a card.

At heart, building evidence involves two deceptively simple questions. How do our data represent the thing we want to study? And what are those data telling us, in those terms? I can show you some fundamental methods for counting, comparing, plotting distributions and finding relationships. Networks are a particularly vivid example: choose what counts as a node and a connection, draw the result, then explore centrality, clustering and communities. Change the representation and the evidence may change with it.

I have spent much of my career using data, mathematical models and network analysis to study biological and political systems. I can show you how to use AI and Python to investigate small political datasets, learning fundamental methods for counting, comparing, visualising and analysing networks as we go. The aim is to build enough practical understanding to produce a simple analysis, explain what its data represent and recognise when its conclusions outrun the evidence.

Core reading

• David Spiegelhalter, The Art of Statistics: Learning from Data (Pelican, 2019).
• Duncan J. Watts, Six Degrees: The Science of a Connected Age (W. W. Norton, 2003).

Developed by military practitioners over two hundred years, wargaming is a practice used regularly by a range of organisations and actors, from the British Army to Greenpeace. What was originally used for training Prussian military officers in the 19th century, giving them a safe space to experiment, practice and make mistakes without serious consequences, has developed as an analytical, experiential and educational tool deployed for an array of contexts, far beyond its original scope. 

For activists, campaigners, civil society groups and those trying to change the world, wargaming offers a crucial practice, one that is becoming increasingly valuable as the legal, financial and human costs of political action increase. Wargaming provides capacity-poor civil society groups, campaigns and activists, a safe-to-fail space to test their assumptions, red team their plans and train organisers, rather than learning on the fly in public.

Wargaming for social change is an emerging field with few practitioners and a limited history. In the UK, examples of what can be done include the situationist ludic-science group Class Wargames, who have prepared wargames to simulate a run on the pound, activist training games focused on negotiating compromise under stress, running a newly elected radical council and games for trade union organising. Red Plenty Games have also produced similar games, such as Parts Per Million and the Social Strike Game.

Meanwhile, in the US and Hungary, political simulations such as 2020 Transition Integrity Project and the 2026 Hungarian Election Simulation have been used to inform pro-democracy coalition strategy in authoritarian contexts. The field of civil society wargaming is as yet still largely undeveloped, which presents a real and exciting opportunity to shape a new field and practice.

I have been playing, designing and facilitating wargames, serious games and recreational megagames for over a decade as a member of Class Wargames and now as an Associate Partner at serious games consultancy Stone Paper Scissors. I can help fellows design and facilitate wargames and serious games and provide guidance on their use. I can also teach the design and adjudication skills so you can run your own, or together as a cohort.

Note - this topic differs from "Building power through play". Whilst those games are focused on communication, persuasion and campaigning, these are for educational, analytical or experiential purposes.

As a Residential Fellow of the first cohort of the Newspeak House Fellowship back in 2016, I have maintained a connection and presence in the Newspeak House space for over 10 years. I lived in the house for 15 months between 2016 and 2017, through a very politically turbulent year marked by the EU Referendum and the 2017 General Election. As a Fellow and member of the NH Faculty, I have engaged with and seen how various cohorts have spent their time in the space. I can offer my thoughts on how best to engage with the Fellowship and how to use your time most effectively in this space.

As the cohorts have changed, so too has the Fellowship, but the fundamentals remain the same - these include: the importance of gathering and convening your communities in the space. Using the building to run events and experiment. Actively managing the community and catalysing opportunities for diverse groups of people and organisations to mix, meet, build friendships and work together. Invite your people, invite your networks to the Ration Club weekly community dinner. Attend as many community events in the college as you can manage and discover new people and practices. Invite communities to run events in the space, introduce them to others and volunteer to help them run events in the space. Immerse yourself in and help strengthen the networks of those within your cohort, the wider Fellowship and existing communities and networking in Newspeak House. Use the space - it’s yours, want to run a conference, speed networking, festival or party - go right ahead. Ground your thinking, experiments and projects in the problems and challenges of existing political and civic communities. Actively reach out to faculty, we’re here to talk things through, assist and get stuck in - invite us.

Be political - Newspeak House is a space for working to build power, technology and the means to build a better world. Be bold, adaptive and experimental in your approach and act with an abundance mindset. You are here to collaborate together as a fellowship, not work in isolation - incubate each other's ideas and be a critical friend. The fellowship is an opportunity for you to explore, dip into things, and draw upon community resource - before contributing to broader civic and political technology impact through your end of year prototypes and projects. As Fellows of Newspeak House, you are stewards of a community resource - water it, nurture it.

If any of this sounds useful, get in touch. Your time here is what you make of it and remember, have fun.

Games are a powerful tool for political change. They are a powerful tool for storytelling, for communicating complex messages and topics in an accessible and fun format. But their real power runs deeper than communication. Games are systems. When people play, they inhabit the magic circle. They suspend the normal rules of the world, and step into spaces where those rules are substituted by the rules of the game - allowing for states of flow and immersion - enhancing learning, creating reflective states, stepping out of the every day, adopting perspectives and roles demanded by the game system.

Games are emerging as a key practice in the playbook for those organising to change politics and are being used by campaigners and activists the world over.

Key examples include Elizabeth Magie’s The Landlord’s Game designed to educate players about the Georgist land value tax. La France Insoumis’ Fiscal Kombat developed to support Jean Luc Melenchon’s bid for the French presidency in 2017. Papers, Please, an influential game about migration and authoritarianism through playing a border guard inspector. Guy Debord’s Game of War designed to train activists in strategic thinking. Games have a key role to play in organising, campaigns and training activists.

I can help fellows design and develop political games. I can hold space for playing political games together, exploring their themes and the means and mechanics they use. This might involve occasional or regular game sessions or game jams to think about games you might wish to create and experiment with.

Nostr came out in 2020 as a decentralised protocol for social networking and publishing. In nostr, there are no servers. There are only clients and relays: clients post messages, relays transfer them to other clients.

Identity in nostr is based on public-key cryptography: your public key is your identity (like in Bitcoin) and your private key is your password. This means that identities are not tied to particular servers, domain names, or anything else. They are just two keys that sign messages.

This is interesting because it's radically simple, it's just identities and relays. How far can you go with such a simple protocol?

Cryptocurrencies have disappeared from the news but at the same time they are becoming part of global finance.

Western economies are introducing regulatory frameworks (MiCA in the EU, GENIUS and Digital Asset Market Clarity Act in the US, FSMA in the UK) and this paves the way for:

• stablecoins (combination of stable value with the properties of a blockchain asset: 24/7 transfer, global accessibility, programmability, composability)
  • ~$8.82 trillion of stablecoins moved in the first six months of 2026
  • ~$36 trillion USDC on-chain volume in H1 2026 (+150% year-over-year)
  • ~$140 billion US gov debt held by USDT
• trading (decentralised exchanges now compete with centralised ones)
  • ~$11B open interest in Hyperliquid
  • ~$767M annualised protocol revenue in Hyperliquid — 12% of CME Group's $6.5B annual revenue (world’s largest derivatives exchange)
• tokenisation of traditional assets
  • BlackRock (world's largest asset manager) launched BUIDL, tokenised money-market/Treasury fund on Ethereum, ~$2.5B in assets by mid-2026
  • JPMorgan Chase (world's largest bank by market cap) launched MONY and JLTXX, two tokenised money-market funds on Ethereum
• lending and credit
  • eg. Aave and Morpho
  • over-collateralised lending, automated liquidations
• automated markets
  • eg. Uniswap, CoW
• prediction markets
  • eg. Polymarket

Dark side:

• smart-contract exploits
• stablecoin depegs
• oracle manipulation
• liquidation cascades

People collectively thinking, discussing, and deciding what they think about an issue is an important aspect of quasidemocratic polities. Ideally, deliberation enables peaceful synthesis of conflicting preferences and needs. Local assemblies are a simple yet dangerous tool to do this. They are simple because it's just people gathering; they are dangerous because unless there is appropriate structure to them, chaos is likely.

Items to research online:

1. Polis by The Computational Democracy Project
2. Community Notes by Twitter
3. vTaiwan's open consultation platform

Books:

1. Open Democracy, Hélène Landemore, 2020

1. When the People Speak, James S. Fishkin, 2011
2. Against Elections, David Van Reybrouck, 2016

Books from a different point of view:

1. Against Democracy, Jason Brennan, 2016
2. The Myth of the Rational Voter, Bryan Caplan, 2007

Finally, there are a number of organisations in the UK (and outside / global) that work in the field of citizen assemblies and participatory democracy:

1. Involve
2. Shared Future
3. Sortition Foundation / 858.org.uk
4. House of the People / timetoassemble.org

I’m fascinated with the AI advancements of the post-ChatGPT world. The world of software engineering is completely revolutionised because of these innovations. Open source has been transcended. I’m excited about these new tools and use them as core tools for my work albeit, indeed, the future is generally substantially unclear.

I've been exploring the Chinese open weights models (Deepseek, Kimi, Minimax, GLM) and local inference AI tools (llama.cpp, ollama, DwarfStar, LM Studio, etc). I find this ecosystem particularly interesting because it currently represents the only meaningful counterweight to the centralized AI future dominated by the three main corporate players (OpenAI, Anthropic, Google).

This topic covers issues of prediction, risk management and forecasting and how these tactics influence the future.

There is a difference between predicting what is likely given where we are now and the current conditions, compared to creating possibility. All too often futurism conflates these two. We'll explore methodologies for collective visioning and the political technologies of future-making.

• The City of Tomorrow: Sensors, Networks, Hackers, and the Future of Urban Life, Carlo Ratti & Matthew Claudel 

• Hegemony Now: How Big Tech and Wall Street Won the World (And How We Win it Back), Jeremy Gilbert & Alex Williams