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junglanml
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junglanml
@danrandow
social sense-making in a meat bag. emerging decentralised governance nerd. On Team @HackHumanityCo
Aotearoa New Zealand Katılım Ocak 2008
2.3K Takip Edilen1.2K Takipçiler
junglanml retweetledi
My latest article about liquid staking on @NEARProtocol with @meta_pool
Arteria Nodes@ArteriaNodes
Just published our NEAR Staking in 2026 guide. We break down how it works now - epochs, seat prices, why 2.5% inflation boosts real yields, and how @meta_pool $stNEAR keeps you flexible while earning. Worth a read whether you’re new or already staking. Check it out 👇
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junglanml retweetledi
The capability story of AI is well understood at this point. Models are faster, cheaper, more capable. Agents can reason across long horizons, use tools, execute multi-step tasks. And wow, are they useful. I'm as addicted to Claude today as I was to Runescape at age 10.
But capability is not the same thing as agency. And the gap between those two things is where most of the interesting problems live.
The deeper promise of AI is that individuals get agents that genuinely act for them. Not for the platform, not for whoever controls the infrastructure. For the user. That's a harder problem than it sounds, and it's mostly been ignored while everyone races to build the fastest model.
For an agent to act on your behalf, it needs to operate in an environment you control. Today, credentials are exposed to the runtime. Execution isn't verifiable at the hardware layer. There's no cryptographic guarantee that your agent is doing what you asked in an environment only you can see. For an agent holding your signing keys, managing your financial transactions, acting autonomously with real stakes—that's a fundamental misalignment between the promise and the architecture.
This is why what @NEARProtocol is building matters. @IronClawAI puts agent execution inside encrypted TEEs with sandboxed tooling—credentials injected at the network boundary, never exposed to the agent itself. Intents lets users express desired outcomes while solvers compete to fulfill them—no single counterparty controls execution. The governance work points in the same direction: systems where no single actor can unilaterally determine outcomes for everyone else.
What I find most important isn't any individual product. It's the coherence of the direction across the stack. Every layer is being designed around the same principle: genuine user sovereignty, enforced architecturally rather than promised contractually.
On the road to agentic commerce, the question is whether the infrastructure layer gets designed around individual agency, or whether we end up with incredibly capable AI that's still ultimately controlled by someone other than the person it's supposed to serve.
IronClaw@IronClawAI
IronClaw now has its own handle. IronClaw is the secure agent harness for the age of always-on AI. Open-source, built in Rust, deployable on @near_ai Cloud, IronClaw ensures your credentials never reach the model. Follow along for demos, security pro tips, new skills, and more.
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Pivoting to posting feet pics now
(Cat feet)
ᑕᗩ₱₱ΞX@CryptoCappex
@alice_und_bob Become just Alice and post photos of bare feet
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@heyshrutimishra Claude Code and Adaptive Thinking both appear twice
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junglanml retweetledi
Honestly, this is the most accurate diagram I've seen.
Waterfall: You plan for 18 months and deliver exactly what nobody needs anymore.
Agile: You deliver something usable at every step, but the CEO keeps asking, "Where's the car?"
AI: You get the car on day one. It has six wheels, the doors are on backwards, and it has a rocket launcher. You spend more time making it yours than actually "building"; it's shaping. owning. verifying. That's what the best AI developers do now. They don't build. They shape and own.
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junglanml retweetledi
As @HackHumanityCo we have worked comprehensively to produce the NEAR - House of Stake Constitutional Documents.
These Constitutional Documents are meant to support a credible path of progressive decentralization: one that works with current legal, technical, and operational realities, while moving House of Stake toward greater autonomy over time. The goal is to build a governance system that can operate responsibly now, earn legitimacy through good decisions, and evolve to better serve the NEAR ecosystem.
To get here has been a co-creative process of many cycles with all key stakeholders involved to set House of Stake up for success.
If you are a @NEARProtocol stakeholder that has locked NEAR to veNEAR in House of Stake you can vote here:
gov.houseofstake.org/proposals/25
Vini B |「 thecoding 」@vinibarbosabr
I voted with 263k veNEAR `FOR` @NEARGovernance's Constitutional Documents it's a batch of 6 documents, discussed individually on gov.near forum for a long time, including via open community calls the documents were built in a real group effort, with a lot of feedback (that were taken into consideration and used to get to this final form) for example, my feedback on the COI policy in a previous version, the COI policy mirrored what is done in some other communities, like Arbitrum, and had, imo, a highly academic approach, far from reality, trying to enforce policies that are not only not enforceable in crypto, but could even be weaponized by bad actors the new COI policy (together with the other documents) are much better now and I believe they constitute one of the best constitutional documents for onchain governance that I know of they are attached to reality, not academy and they will contribute to making HoS one of the best governance protocols in crypto, as long as it is able to see higher participation thresholds and more staked $NEAR (to veNEAR) the documents are: + HSP-008 Constitution + HSP-009 Proposals and Voting Procedures (PVP) + HSP-010 Screening Committee Charter (ScrCC) + HSP-011 Mission Vision Values (MVV) + HSP-012 Conflict of Interest Policy (COIP) + HSP-013 Code of Conduct (CoC)
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junglanml retweetledi
Confidential swaps are now live on near.com! Confidentiality is the key unlock for onchain adoption.
@near_intents is now the first cross-chain execution layer with protocol-level confidentiality. 35+ chains. Non-custodial. Not a wrapper, not a mixer. Users can share viewkeys optionally for auditing or compliance.
You no longer have to choose between onchain and privacy. Until now, onchain meant making everything transparent. That changes with NEAR's confidential shard. Now you can trade without anyone knowing what assets you have, what you're trading, and what you're paying for.
Go dark on near.com ⚫️
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Claude has a Sunday today. It’s basically in hangover mode and just making up answers on the go
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@timClicks Sorry to hear that, @timClicks. Hope the fire is starting to burn within again 🔥 <– my focus
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Hello, Internet. Sorry that I haven't been here for a while. It turns out that burnout can get as bad as they say. How are you doing?
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It's a privilege to be part of the @HackHumanityCo team who developed the NEAR - House of Stake Constitutional Documents.
Klaus Brave (❤️,⚡️)@klausbrave
As @HackHumanityCo we have worked comprehensively to produce the NEAR - House of Stake Constitutional Documents. These Constitutional Documents are meant to support a credible path of progressive decentralization: one that works with current legal, technical, and operational realities, while moving House of Stake toward greater autonomy over time. The goal is to build a governance system that can operate responsibly now, earn legitimacy through good decisions, and evolve to better serve the NEAR ecosystem. To get here has been a co-creative process of many cycles with all key stakeholders involved to set House of Stake up for success. If you are a @NEARProtocol stakeholder that has locked NEAR to veNEAR in House of Stake you can vote here: gov.houseofstake.org/proposals/25
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junglanml retweetledi
Anthropic just released the most IMPORTANT chart in the AI labor debate.
This comes from the company that builds Claude using data from 2 million real conversations.
Here’s what it shows.
The blue area is every task AI could theoretically do right now.
The red area is what people are actually using it for.
The gap between them is enormous and that gap is your career runway.
Computer programmers are already 75%
covered.
Customer service reps, data entry workers, financial analysts, they’re next.
But here’s what no one is talking about.
The mass layoffs haven’t really started.
Unemployment for exposed workers hasn’t budged.
So what’s actually happening?
Companies are closing the front door, hiring for workers aged 22 to 25 in AI exposed jobs has dropped 14%.
The most exposed workers aren’t factory workers, they’re college educated, higher earning.
49% of US jobs now have at least a quarter of their tasks inside AI’s reach.
That’s up from 36% just one year ago.
And the red area on that chart,
the real world usage is still a fraction of what’s possible.
Every month, it grows a bit.
Anthropic built the scoreboard and most people haven’t looked at it yet.
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junglanml retweetledi
@ilblackdragon I made one too. Felt like it should have always felt.
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just made a swap on near.com
was 100x easier than any other platform i've used to date
David Mirzadeh@mirzdame
just made a swap on near.com was 100x easier than any other platform i've used to date
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This @kusamanetwork staking reward message says two important things: 1 Crypto supports micro transactions, and 2 someone with $10 can get 15% APY.
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@PryvitKyle "the highest-scoring AI users [...] used AI as a thinking partner, not a replacement for thinking"
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JCR Licklider called this out in the 1950s where he hypothesized man-computer symbiosis. The question is whether you’re forming a mutualism or parasitism symbiotic relationship with the LLMs?
groups.csail.mit.edu/medg/people/ps…
Alex Prompter@alex_prompter
Anthropic's own researchers just proved that using AI to learn new skills makes you 17% worse at them. and the part nobody's reading is more important than the headline. the paper is called "How AI Impacts Skill Formation." randomized experiment. 52 professional developers. real coding tasks with a Python library none of them had used before. half got an AI assistant. half didn't. the AI group scored 17% lower on the skills evaluation. Cohen's d of 0.738, p=0.010. that's a real effect. and here's what makes it sting: the AI group wasn't even faster. no significant speed improvement. they learned less AND didn't save time. but the viral framing of "AI bad for learning" misses what actually matters in this paper. the researchers watched screen recordings of every single participant. they identified 6 distinct patterns of how people use AI when learning something new. 3 of those patterns preserved learning. 3 destroyed it. the gap between them is enormous. participants who only asked AI conceptual questions scored 86% on the evaluation. participants who delegated everything to AI scored 24%. same tool. same task. same time limit. the difference was cognitive engagement. the highest-scoring AI users actually outperformed some of the no-AI group. they asked "why does this work" instead of "write this for me." they generated code then asked follow-up questions to understand it. they used AI as a thinking partner, not a replacement for thinking. the lowest-scoring group did what most people do under deadline pressure: pasted the prompt, copied the output, moved on. they finished fastest. they learned almost nothing. and here's the finding that should concern every engineering manager alive: the biggest score gap was on debugging questions. the skill you need most when supervising AI-generated code is the exact skill that atrophies fastest when you let AI do the work. the control group made more errors during the task. they hit bugs. they struggled with async concepts. they got frustrated. and that struggle is precisely what built their understanding. errors aren't obstacles to learning. they ARE learning. removing them with AI removes the mechanism that creates competence. participants in the AI group literally said afterward they wished they'd "paid more attention" and felt "lazy." one wrote "there are still a lot of gaps in my understanding." they could feel the hollowness of having completed something without understanding it. that's not a productivity win. that's debt. this paper isn't an argument against using AI. it's an argument against using AI unconsciously. Anthropic publishing research showing their own product can inhibit skill formation is the kind of intellectual honesty the industry needs more of. the practical takeaway is simple: if you're learning something new, use AI to ask questions, not to skip the work. the struggle is the product.
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Great interview with @alice_und_bob
Tommi positions @Polkadot – still focused on web3 values, good tech, recovering from the OpenGov period of overspending, pivoting to a product-led strategy, lots still to be revealed
PolkaWorld@polkaworld_org
In an industry where Web3 narratives shift by the season, some chase the next headline. Others pause, and ask what future they truly want to help build. A year later, we met Tommi @alice_und_bob again in Hong Kong. Having recently concluded his formal role with the @Web3foundation, he is stepping into a new chapter in both life and career — continuing to deepen his engagement with the @Polkadot ecosystem while also feeling the pull of the rapidly advancing AI wave. His focus remains on on-chain governance and public infrastructure, even as he explores new possibilities with a greater sense of independence. This wasn’t merely a conversation about “what’s next” in a career. It became a deeper reflection on where Web3 is actually heading, how technology is meant to be used, and what kind of culture and products @Polkadot 's “Second Age” truly demands. From AI reshaping the developer paradigm, to why on-chain applications remain trapped in financial narratives; from infrastructure reaching maturity, to adoption that still lags behind — Tommi revisits the industry’s most fundamental question with rare clarity: If the technology is finally ready, are we ready to build something that truly matters? Read PolkaWorld’s latest interview below. 0:00 See you again 0:54 Current Chapter 2:11 The AI-Driven Shift in Development 4:19 Technical Maturity vs. Market Recognition 6:47 Hub Liquidity & Developer Migration 9:34 Has Web3 dApp Innovation Stalled? 12:24 Driving Adoption on @Polkadot Hub 16:44 Builder Culture as the Deciding Factor 18:35 Polkadot’s Core Value — Freedom 22:20 The Hard Reset 29:13 Why Still Believe in @Polkadot 31:20 Hong Kong as an Industry Connector 34:53 Bear Market Mindset: Cycles, Reality, and Tech Long-Termism 38:53 After AI Took the Spotlight: Is Web3 Left Behind or Embedded in the Future? 42:02 The Uncertainty Heading into 2026
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@meetwithhq I do not, but this involves one of many centralisation tradeoffs
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@danrandow It's interesting to learn about your pivot to DAOs.
I'm curious if you struggle with coordinating meetings with multiple teams/groups.
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junglanml retweetledi
What is left when AI runs it all?
In this @Unchained_pod, @mikejcasey and @DMattin join me to discuss:
💡 How Moltbook points to where the AI meta is headed
😬 How AI could impact jobs
❕️ Which country is best positioned to win the AI race
⁉️ What a post-human economy looks like
👀 Which jobs survive in a post-human economy
Timestamps:
🚀 0:29 Introduction
🧏♂️ 2:10 How the Moltbook saga offers a window into where the AI meta could be headed
🤔 9:29 Why Michael wants a sovereign AI model
🌎 17:42 How AI could impact jobs
⚠️ 25:31 How AI could have a worse effect on the mental health young people than social media
❕️ 30:27 Which country is best positioned to win the AI race?
📍 35:02 What money looks like in a post-human economy
🤔 52:00 Which jobs flourish in a post-human economy?
💡 1:00:33 Michael and David share tokens and projects they find intriguing
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Wise words from @lrettig "the world is paying much more attention to AI than to crypto in this moment, for good reason. But crypto and AI are a marriage made in heaven, and what’s good for one is good for the other" rettig.substack.com/p/nine-years-i…
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