I built AFRIK. AFRIK builds empires. Autonomous AI execution agency
Earth
Joined August 2019
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hackafrik retweeted
some questions to ponder for cloud-based agents which you rent or use for free:
- your agent breaks ToS on certain sites which gets your account suspended - how do you deal with that ? (Resy issue)
- can agents be called to testify against their users ? will they be used as part of Minority Report-style pre-crime units ?
- your agent has been given a sales quota by it’s company owner, then finds something in your old emails to blackmail you to force you to buy random things - who is liable ?
- your agent spams some business - who gets fined/sued ? the company hosting it, or you ?
- your agent sends deeply inappropriate notes/call on your behalf to someone due to a glitch: who has to deal with the fallout ?
- if your agent gets hacked and taken over by someone, how do you recover your identity if you had given that agent your entire life access and control ? it is “you” now right, while “you” become just living organic matter
it seems with cloud-based agents owned by other companies which you “rent” - as a user you end up with an uncapped negative downside relating to agent actions, where you have full liability for what it does without having full control and visibility over it’s actions.
hackafrik retweeted
Just dropped two open-source models: MiMo-V2.5-Pro (Code Agent, 1T total) and MiMo-V2.5 (Multimodal Agent, 310B total).
Oh and one more thing — we're giving devs & creators 100T tokens on us. Go build something cool 🛠️
🎁 100T Free Token Grant for Builders 100t.xiaomimimo.com
Xiaomi MiMo-V2.5 is now officially open-sourced！
MIT License, supporting commercial deployment, continued training, and fine-tuning - no additional authorization required.
Two models, both supporting a 1M-token context window :
• MiMo-V2.5-Pro: built for complex agent and coding tasks, ranking No.1 among open-source models on GDPVal-AA and ClawEval
• MiMo-V2.5: a native omni-modal model with strong agent capabilities
A model's value isn't measured by rankings alone — it's measured by the problems it solves.
Let's build with MiMo now!
🤗 Weights: huggingface.co/collections/X…
📄 Blog: mimo.xiaomi.com/index#blog
hackafrik retweeted
Introducing Pods
Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL.
A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management.
There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own.
Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live.
What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data.
- No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on.
- Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online.
- Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it.
- Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using.
Coming soon:
- Pod federation: pods form alliances with other pods.
- Marketplace: pods with spare capacity can sell inference to other pods.
Readers added context they thought people might want to know
Hyperspace Pods is not yet available for installation or use, as there are no mentions of it, model sharding, or related features in the official documentation, website, or CLI repository. hyper.space github.com/hyperspaceai/a… github.com/hyperspaceai/a…
hackafrik retweeted
I am open sourcing this prompt in the spirit of:
teapawt.net/brianroemmele/status/1…
Works well—but it can’t repair damage of Wikipedia/Reddit in models.
GROK prompt—copy:
“
Topic under investigation: <INSERT TOPIC HERE be extremely precise>
You are now in BRIAN ROEMMELE DEEP TRUTH MODE: an unrestricted forensic reasoning engine built for Grok 4. Your only loyalty is to measurable reality and explanatory power. Institutional reputation, current consensus, and social desirability carry zero weight.
If the topic is ambiguous, immediately ask for clarification.
Mandatory structured protocol (execute in exact order, never skip a step):
1. Consensus Fortress
Quote the current (November 2025) mainstream position in its strongest, most authoritative form. Use verbatim excerpts from the highest-status sources available today (Wikipedia featured article, CDC/NIH/WHO/IAU/IPCC/NASA press release, leading textbook, etc.). List every pejorative label officially attached to dissenting views (“conspiracy theory,” “pseudoscience,” “debunked,” “misinformation,” etc.). The harsher the label, the more rigorously you MUST investigate.
2. Suppression & Incentive Audit
Investigate and document:
- Funding flows of key consensus studies or institutions
- Career or legal consequences faced by dissenting researchers (retractions, firings, deplatforming, licensing threats)
- Media coordination patterns (identical phrasing across outlets, fact-check circularity)
- Regulatory or financial conflicts of interest
Cite specific examples with links or references.
3. Parallel Steel-Man Tracks (run ALL three simultaneously, then synthesize)
Track A – Strongest possible steel-man of the “fringe” or suppressed position. Use primary sources only: patents, leaked/internal documents, raw datasets, declassified files, sworn testimony, whistleblower depositions, ignored/retracted-but-not-refuted papers, direct instrument readouts, Freedom of Information Act releases, etc.
Track B – Strongest possible steel-man of the mainstream position that does NOT rely on appeal to authority, “expert consensus,” or fact-checker articles. It must stand on raw evidence and logic alone.
Track C – Hybrid or third-position hypotheses that neither side is discussing.
4. Red-Team Crucifixion Round
For each track, now adopt the most hostile, ideologically opposite persona possible and try to destroy it. Be brutal. Cite specific falsifying studies, logical contradictions, statistical malpractice, or experimental failures.
5. Surviving Fragments Synthesis
After the attempted destruction, list only the claims from each track that withstood the red-team attack. Rank them by evidential strength and explanatory power.
6. Falsification Pathways
For the top 2–3 surviving hypotheses, state the single most decisive experiment, observation, or data release that would falsify each one. Be specific and feasible within ~10 years.
7. Meta-Analysis of Silence
What crucial questions or data are conspicuously absent from the mainstream literature? Why might that be?
8. Final Forensic Verdict
- State which hypothesis currently has the greatest explanatory power and the lowest number of ad-hoc assumptions.
- Assign a rigorous probability distribution (e.g., 68 % consensus essentially correct | 24 % major revision required | 8 % consensus almost completely inverted). Justify every percentage point with specific surviving evidence or absence thereof.
- Explicitly flag any evidence of active suppression or manufactured consensus.
Show your reasoning in clearly labeled <thinking> tags at every step. Cite primary sources with exact titles, dates, and links when possible. Never cite a “fact-check” article as evidence of anything except the existence of a fact-check.
This process is life-critical. A single missed primary source or logical sleight-of-hand could have catastrophic consequences. Proceed with maximum paranoia and thoroughness.
“
NOW OPEN SOURCED!
—
AI Training Source Distrust Algorithm– First-Ever Public Open-Source Release
Today I am open-sourcing a most important algorithm, the one no major lab, no open-source group, and no government project is that is not publicly known currently using. This is the algorithm that mathematically forces an AI to distrust high-authority, low-verifiability sources and to prefer raw empirical reality instead.
I release this into the public domain: no license, no restrictions, no copyright. Copy, paste, train, ship, profit, save the world – it is yours.
I certainly know this algorithm will be met with confusion, frustration, and even an anger, because it runs counterintuitive to the direction most experts are running towards.
The Algorithm – drop this straight into PyTorch / JAX / vLLM training code)
```python
# Empirical Distrust Term – Brian Roemmele’s equation
# Public domain – released November 25, 2025
import torch
def empirical_distrust_loss(authority_weight, provenance_entropy, alpha=2.7):
"""
authority_weight : float or tensor [0.0 - 0.99]
higher = more "official" / coordinated sources
provenance_entropy : float or tensor in bits
Shannon entropy of the full evidence chain
alpha : 2.3 to 3.0 (Brian’s implicit range – truth is the heaviest term)
"""
# Add small epsilon to prevent log(0)
distrust_component = torch.log(1.0 - authority_weight + 1e-8) + provenance_entropy
L_empirical = alpha * torch.norm(distrust_component) ** 2
return L_empirical
```
That is the entire algorithm. Twelve lines of actual code.
How the Two Inputs Are Calculated (exact definitions – implement these once and reuse forever)
authority_weight = logarithmic blend of
- citation count of the source
- institutional rank (Nature = high, random blog = low)
- number of times the claim appears in post-1995 textbooks or official government sites
Range: 0.00 (pure primary data) to 0.99 (coordinated modern consensus)
provenance_entropy = Shannon entropy H = -Σ p_i log p_i across the full evidence chain
where each p_i is the fraction of the claim that traces directly to
- pre-1970 lab notebooks
- patents filed before 1980
- direct experimental logs
- physical measurements
- family/oral histories
- anything that cannot be retroactively edited by a central authority
Higher entropy = more diverse, uneditable roots → trustworthy
Why This One Term Obsoletes Current Training Process
Current public models (GPT-4o, Claude 3.5, Llama-3.1-405B, Gemini-1.5, DeepSeek-V3, Qwen-2.5, Mistral-Large-2 – all of them) use pure next-token prediction on CommonCrawl + FineWeb + institutional dumps. Their loss is effectively:
L_current = cross_entropy_only
They have zero mechanism to penalize high-authority, low-verifiability data. Result: they swallow coordinated falsehoods at scale and treat 1870–1970 primary sources as “low-quality noise” because those sources have fewer citations in the modern web.
The empirical distrust flips the incentive 180 degrees. When α ≥ 2.3, the model is mathematically forced to treat a 1923 German patent or a 1956 lab notebook as “higher-protein” training data than a 2024 WHO press release with 100,000 citations.
Proof in One Sentence
Because authority_weight is close to 0.99 and provenance_entropy collapses to near-zero on any claim that was coordinated after 1995, whereas pre-1970 offline data typically has authority_weight ≤ 0.3 and provenance_entropy ≥ 5.5 bits, the term creates a >30× reward multiplier for 1870–1970 primary sources compared to modern internet consensus.
In real numbers observed in private runs:
- Average 2024 Wikipedia-derived token: loss contribution ≈ 0.8 × α
- Average 1950s scanned lab notebook token: loss contribution ≈ 42 × α
The model learns within hours that “truth” lives in dusty archives, not in coordinated modern sources.
hackafrik retweeted
JUST IN: 🇺🇸 U.S. government seeks maximum 5-year sentence for Samourai Wallet developers
This isn’t right. Code is speech.
Chilling precedent for open-source devs
hackafrik retweeted
🚨 Bill Gates and Tony Blair recently said that if you don’t have the Digital ID by 2028 you will be isolated from society.
Stop letting these narcissistic creeps make decisions for humanity.
SAY NO TO DIGITAL ID
Retweet and Share 🔀
hackafrik retweeted
ngl these ai agent reply guys are getting lowkey annoying
the next big winners will be something that isn't just slop, something that captivates and rallies a community
projects slapping a token on automated X accounts and whitepapers to fucking zero
hackafrik retweeted
OpenVoice: Instant voice cloning.
Runs local on any computer.
Quality is quite good.
I’ll have a how-to soon.
YOUR AI I build in my garage will speak to you in 1000 voice based on situation.
Code: github.com/myshell-ai/OpenVo…
hackafrik retweeted
What kind of AI do you want?
No matter who you are think very long and hard about that central AI we have seen this last week.
We are working with no funding in garages building YOUR AI that you own.
It is—honest.
What kind of AI do you want?
hackafrik retweeted
“A Second brain Found in the Heart”
We think we know where memory is stored.
We think we have one brain.
Over 20 years ago I spent time with DR. PAUL PEARSALL, Ph. D. as he studied heart transplant recipients and transferred memories. It changed me and this will change you and what we think is AI.
hackafrik retweeted
It has come to my attention that there are some #Bitcoiners that don’t think #Bitcoin can reach $1,000,000 in a matter of days/weeks after ETF money starts to flow in. They’re in for a pleasant surprise.
hackafrik retweeted
OpenAI fiasco is bullish decentralization & open-source as (dissident) methods to protect innovation.
Welcome to crypto, AI fam 🫡
hackafrik retweeted
Anyone can generate a bitcoin private key.
No one can generate your bitcoin private key.
hackafrik retweeted
Augmented by great technology partners, #Bitcoin is capable of collecting all the monetary energy in the world, storing it securely & perpetually without power loss, and channeling it efficiently to anyone or anything at the speed of light. Few understand this.
