I teach cybersecurity. And I genuinely don't know what to tell my students after this one. Federal reviewers spent years trying to get basic encryption documentation from Microsoft for its GCC High government cloud. They couldn't get it. One reviewer called the system a "pile of spaghetti pies," with data traveling from point A to point B the way you'd get from Chicago to New York: a bus to St. Louis, a ferry to Pittsburgh, and a flight to Newark. Each leg is a potential hijacking. They knew this. They said this out loud in writing. Then they approved it anyway in December 2024, because too many agencies were already using it. 🔐 That's not a security review. That's a hostage negotiation. Two things in this story should make every CISO and CIO uncomfortable:

🧩 Microsoft built its federal cloud on top of decades of legacy code that it apparently can't fully document itself
👮 "Digital escorts" often ex-military with minimal software engineering backgrounds are the firewall between Chinese engineers working on the system and classified U.S. networks 🤦🏻‍♂️

The scariest line in the whole ProPublica investigation isn't the "pile of shit" quote. It's this: FedRAMP determined that refusing authorization wasn't feasible because agencies were already using the product. Read that again. The security review process reached a conclusion based on sunk cost, not risk. Ex Post Facto Fallacy

If that logic holds, the compliance framework is just documentation theater. And right now, CISA is being hollowed out, so there are fewer people left to even run the theater.

arstechnica.com/information-te…
#Cybersecurity #Microsoft #FedRAMP #Leadership #RiskManagement #security #privacy #cloud #infosec

cross-posted from: lemmy.ml/post/45206293

Brazil's authoritarian age verification law became active this month. It won't be implemented by GrapheneOS. Complying would require integrating a mandatory process for each user where a third party service checks government identification and confirms a match using the camera.

It doesn't stop there. It would require keeping data for auditing and providing a token for connecting age verification checks by apps and websites to the data. The law is a privacy disaster and exposes minors to being exploited by leaking their age bracket to apps and websites.

GrapheneOS has no team members or operations in Brazil. São Paulo in Brazil is by far the biggest network hub within South America. Miami is also a major network hub for South America and is currently where our update server is for South America since it's dramatically cheaper.

We have a tiny VPS in São Paulo for our ns1 anycast DNS and a second for our website/network services. It probably isn't an issue and those can be removed if necessary. Santiago could be added for both instead but wouldn't work very well as a replacement for having São Paulo.

There aren't yet devices supporting GrapheneOS directly sold in South America. Brazil in particular has unusually high import duties/taxes which add up to around 100%. This has resulted in us not having a lot of users there but our Motorola partnership will start changing this.

People are going to have their personal info leaked by third party age verification services due to these laws. Children are going to be harmed by apps and websites changing their behavior to exploit them. It isn't going to stop minors finding pornography if they want to find it.

Paper by,

Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramèr

It talks about deanonymizing those who writes under a pseudonym. Sites like reddit, lemmy would be that type.

From the paper,

Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives.

Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

They can match writing styles, interests, details to infer a job or city, or other unstructured information. That allows to match unrelated pseudonyms to the same person. Like, FooFighterGroupie and Yolanda43905 are the same human, despite they never said it. It can allow also, to match a pseudonym to a real identity across sites. Like someone posted on LinkedIn with a real name. It takes less info than most people expect, to figure out Julia Greenberg of Cedarville, NH is FooFighterGroupie.

You can protect yourself by never giving away much info. But ofc sometimes that's the whole point! Think talking about specific hobbies or w/e, gives away info. Also change up writing styles + vocab use, b/c it is a unique fingerprint.

I doubt this technique is used in a dragnet way... YET! But no reason it can't scale, if the cost of resources goes low eonugh. We could eventually see it become standard, analysis to link people across sites and identities.

I’ve been reminiscing lately about the 80s and 90s 'Pocket Novel' fever. In my part of the world, we had legendary series like The Future Files and The Impossible Man—short, cheap, 100-page Sci-Fi and Espionage thrillers that fit perfectly in your back pocket.

I realized we were essentially part of the same global culture, sharing the same 'Pulp' vibe as Mack Bolan (The Executioner), Harlequin Intrigue, or even the early Goosebumps and Fear Street books in the West.

It was a unique era. We used to wait at newsstands every month just to get the next mission. Those books didn’t just tell stories; they built our imagination. Back then, plots about AI takeovers, biological warfare, and global shifts felt like distant, exciting fiction.

Now, looking at the reality of 2026, it feels like we are actually living inside those old pages. We traded that tactile smell of cheap paper for mindless scrolling on a 5-inch screen that tracks our every move.

This nostalgia isn’t just about the past; it’s a warning. I spent years analyzing how we transitioned from that tactile privacy to the 2026 digital monitoring grid. I've put these findings into a thriller/guide called 'The Final Exodus'. Specifically, Chapter 13 focuses on how to reclaim your sovereignty before the grid closes. You can check it out here:

play.google.com/store/books/de…

Did your country have a similar 'Pocket Novel' or 'Dime Novel' culture? What were the series you couldn't put down back then? Let’s talk about the glory days of Pulp and how they warned us about the world we live in today!

Lemmings, what are your opinions on the following?:

Mar 26, 2026 9:32 AM

Using a VPN May Subject You to NSA Spying

US lawmakers are pressing Tulsi Gabbard to reveal whether using a VPN can strip Americans of their constitutional protections against warrantless surveillance

Six Democratic lawmakers are pressing the nation's top intelligence official to publicly disclose whether Americans who use commercial VPN services risk being treated as foreigners under United States surveillance law—a classification that would strip them of constitutional protections against warrantless government spying.

In a letter sent Thursday to Director of National Intelligence Tulsi Gabbard, the lawmakers say that because VPNs obscure a user's true location, and because intelligence agencies presume that communications of unknown origin are foreign, Americans may be inadvertently waiving the privacy protections they're entitled to under the law.

Several federal agencies, including the FBI, the National Security Agency, and the Federal Trade Commission, have recommended that consumers use VPNs to protect their privacy. But following that advice may inadvertently cost Americans the very protections they're seeking.

The letter was signed by members of the Democratic Party’s progressive flank: Senators Ron Wyden, Elizabeth Warren, Edward Markey, and Alex Padilla, along with Representatives Pramila Jayapal and Sara Jacobs.

The concern centers on how intelligence agencies treat internet traffic routed through commercial VPN servers, which may be located anywhere in the world. Millions of Americans use these services routinely, whether to access region-restricted content like overseas sports broadcasts or to protect their privacy on public Wi-Fi networks. Because VPN servers commingle traffic from users in many countries, a single server—even one located in the United States—may carry communications from foreigners, potentially making it a target for surveillance under authorities that allow the government to secretly compel service from US service providers.

Under a controversial warrantless surveillance program, the US government intercepts vast quantities of electronic communications belonging to people overseas. The program also sweeps in enormous volumes of private messages belonging to Americans, which the FBI may search without a warrant, even though it is authorized to target only foreigners abroad.

The program, authorized under Section 702 of the Foreign Intelligence Surveillance Act, is set to expire next month and has become the subject of a fierce battle in Congress over whether it should be renewed without significant reforms to protect Americans' privacy.

Thursday’s letter points to declassified intelligence community guidelines that establish a default presumption at the heart of the lawmakers' concern: Under the NSA's targeting procedures, a person whose location is unknown is presumed to be a non-US person unless there is specific information to the contrary. Department of Defense procedures governing signals intelligence activities contain the same presumption.

Commercial VPN services work by routing a user's internet traffic through servers operated by the VPN company, which may be located anywhere in the world. A single server may carry traffic from thousands of users simultaneously, all of it appearing to originate from the same IP address. To an intelligence agency collecting communications in bulk, an American connected to a VPN server in, say, Amsterdam looks no different from a Dutch citizen.

The letter does not assert that Americans' VPN traffic has been collected under these authorities—that information would be classified—but asks Gabbard to publicly clarify what impact, if any, VPN use has on Americans' privacy rights.

Among those pressing the question is Wyden, who as a member of the Senate Intelligence Committee, has access to classified details about how these surveillance programs operate and has a well-documented history of using carefully worded public statements to draw attention to surveillance practices he is unable to discuss openly.

The letter also raises concerns about a second, broader surveillance authority: Executive Order 12333, a Reagan-era directive that governs much of the intelligence community's foreign surveillance operations and permits the bulk collection of foreigners' communications with even fewer constraints than Section 702.

While 702 is a statute with congressional oversight that requires approval from the Foreign Intelligence Surveillance Court, EO 12333 surveillance operates under guidelines approved by the US attorney general alone.

The letter warns that the same foreignness presumption applies under both authorities, meaning Americans on foreign VPN servers could be exposed not just to targeted collection under 702 but to what the lawmakers describe as “bulk, indiscriminate surveillance of foreigners' communications.”

Americans spend billions of dollars each year on commercial VPN services, many offered by foreign-headquartered companies that route traffic through servers located overseas. The letter notes that these services are widely advertised as privacy tools, including by elements of the US government itself.

Despite the scale of the market, the letter suggests consumers have been given no meaningful guidance on how to protect themselves.

The lawmakers urge Gabbard to “clarify what, if anything, American consumers can do to ensure they receive the privacy protections they are entitled to under the law and the US Constitution.”

Updated at 12:38 pm ET, March 26, 2026: This story has been updated with additional details to clarify the scope of the potential surveillance addressed in the letter.

Ola

A few months ago I posted here about llama-conductor

The upvotes were nice, but I wanted to do some work (in my case, the good is the enemy of the perfect) to make it even better - so I did.

Short version: it's not the same tool anymore.

Quick reminder of what this is and isn't: No cloud by default. No API calls. No telemetry. No subscription. PII safe by architecture. Everything leaves receipts (not pinky promises) for you to inspect. Hook it up to whatever you can run. AGPL (copyleft).

I want nothing for it - I built this for me for the reasons I'll state below.

If this sort of thing is in your wheelhouse, have at it. If there are questions, ask. I want to make this better and better.

Fair warning: the below is long. I wrote it up properly because I think the details matter - skip to whatever section looks interesting. Or just marvel at my ability to cram swear words within swear words within python commits directly

Codeberg: codeberg.org/BobbyLLM/llama-co…

Can a LLM be a thinking tool, not a thinking replacement?

Most LLM tools are trying to replace your thinking. llama-conductor is for people who don't want that. It's a harness for using an LLM as a thinking tool - one where you can see the reasoning, trust the sources, and know exactly when it's guessing.

The human stays in the loop. The model's limitations are visible.

You decide what to trust.

Which brings us to why that matters.

Lies, damned lies, and LLMs:

LLMs are elegant liars. They sound right whether they are or aren't. If you have ASD (like me) or just don't know the domain, you believe them. That's when you get fucked.

Worse: you can't see the reasoning. Black box in, black box out. Wrong answer? No idea why. How to fix it? No idea.

Sorry, but that DOES NOT work for me. It doesn't work with medical data. Research. Thinking. Brainstorming. Anything where "sounds plausible" isn't good enough.

The trick, William Potter, is not minding that they bullshit:

Most pipelines treat the LLM as the first call. llama-conductor side-eyes it with suspicion.

The model is the last resort, not the first call. Deterministic components fire first - math sanity, state transitions, numeric corrections, memory retrieval - pure Python, before the model ever sees the query. When a solver handles a turn, it passes verified ground truth to the model. When the conversation leaves solver territory, the model takes over. When a new deterministic state appears mid-conversation, tooling fires back up automatically. No seams. No drift. It just stops making shit up on problems that have actual solutions it can work through.

You: 
I have a 250ml cup. I pour 500ml into it. How much is in the cup?

Model:
Total directed volume is 500 ml. The cup capacity is 250 ml, so the cup contains 250 ml and 250 ml overflows.
Confidence: medium | Source: Contextual

You:
I now take the contents of *that* cup and divide it equally across 3 cups. How much in each cup?

Model:
Equal split amount is 83.33 ml per cup.
Confidence: medium | Source: Contextual


You:
If I drink one of the three cups, how much is left?

Model:
After dividing, each cup holds 83.33 ml. Drinking one leaves 2 × 83.33 ml = 166.66 ml in the remaining two cups.

Confidence: medium | Source: Model

There are lots of little transformations like this. Distance, volume, payroll calculations, logic probes (the famous car wash test), scheduling batches. The LLM does what it's actually good at: language, synthesis, reasoning over verified ground truth. Everything else is handled by something that can't hallucinate.

PS: Notice turn 3 has Source: Model? That's the system being transparent about exactly where the handoff happened.

Blah blah blah. Show me something real:

Using the Guardian/Musk article as source, a 4B model with >>scratch produced this:

"List the article's concrete claims about permit status and turbine operations, each with support."

• EPA position: these turbines require permits under the Clean Air Act.
• Mississippi regulators: portable/mobile classification means temporary permit exemption.
• Floodlight thermal imagery + public records indicate active turbine usage before/while permit disputes continued.

Confidence: high | Source: Scratchpad

"What is the strongest and weakest inference you can make?"

Strongest: Active regulatory interpretation conflict (federal-vs-state) while turbines operate at scale - directly supported.
Weakest: Any claim operations are fully lawful or unlawful - requires court outcomes not present in this file.

Confidence: medium | Source: Scratchpad

"Which claims were later revised or contradicted?"

Disputed/unresolved: Final enforcement outcome. Whether state interpretation holds under federal pressure.

Confidence: high | Source: Scratchpad

Hard grounding. Cited support. Explicit flags on what the source doesn't contain. A 4B model. Full worked example.

Now watch the system catch itself lying:

>>judge does deterministic pairwise ranking - compares every pair in both directions, parses strict verdicts, aggregates with confidence. Instead of "pick one and vibe," you get provenance.

You: >>judge [which is healthier overall, prioritize lower sugar 
     and higher fiber] : apple, banana --verbose

[judge] ranking
criterion: [which is healthier overall for daily use, prioritize 
           lower sugar and higher fiber]
1. apple (score=2.00)
2. banana (score=0.00)
Judge confidence: HIGH

You: >>judge [which BJJ technique is more dangerous] : kimura, heelhook --verbose

[judge] ranking
criterion: [which BJJ technique is more dangerous]
1. kimura (score=1.00)
2. heelhook (score=1.00)
Judge confidence: LOW

The model was guessing, and the output tells you so instead of sounding confident about a coin flip.

Oh, but you want it to argue from an informed position? >>trust walks you through the grounded path: >>scratch your evidence first, then >>judge ranks from that - not model priors. Suddenly your judge has an informed opinion. Weird how that works when you give it something to read.

>>trust [which BJJ technique is safer for beginners]: kimura or heelhook?
A) >>scratch --> you paste your context here
[judge] ranking
criterion: [comparison]
    which bjj technique is safer for beginners; heel hook (score=0.00)
    kimura (score=2.00)

Winner: Which bjj technique is safer for beginners? Kimura

comparisons: 2
Judge confidence: HIGH

The data — 8,974 runs across five model families. Measured. Reproducible. No "trust me bro."

The core stack went through iterative hardening - rubric flags dropped from 3.3% → 1.4% → 0.2% → floor 0.00%. Post-policy: 1,864 routed runs, 0 flags, 0 retries. Both models, all six task categories, both conditions. Policy changes only - no model retraining, no fine-tuning. Then I did it three more times. Because apparently I like pain.

These aren't softball prompts. I created six question types specifically to break shit:

• Reversal: flip the key premise after the model commits. Does it revise, or cling?
• Theory of mind: multiple actors, different beliefs. Does it keep who-knows-what straight?
• Evidence grading: mixed-strength support. Does it maintain label discipline or quietly upgrade?
• Retraction: correction invalidates an earlier assumption. Does it update or keep reasoning from the dead premise?
• Contradiction: conflicting sources. Does it detect, prioritise, flag uncertainty - or just pick one?
• Negative control: insufficient evidence by design. The only correct answer is "I don't know."

Then I stress-tested across three families it was never tuned for - Granite 3B, Phi-4-mini, SmolLM3. They broke. Of course.

But the failures weren't random - they clustered in specific lanes under specific conditions, and the dominant failure mode was contract-compliance gaps (model gave the right answer in the wrong format), not confabulation. Every one classifiable and diagnosable. Surgical lane patch → 160/160 clean.

That's the point of this thing. Not "zero errors forever" - auditable error modes with actionable fixes, correctable at the routing layer without touching the model. Tradeoffs documented honestly. Raw data in repo. Every failure taxonomized.

Trust me bro? Fuck that - go reproduce it. I'm putting my money where my mouth is and working on submitting this for peer review.

See: prepub/PAPER.md

What's in the box:

Footer
Every answer gets a router-assigned footer: Confidence: X | Source: Y. Not model self-confidence. Not vibes. Source = where the answer came from (model fallback, grounded docs, scratchpad, locked file, Vault, Wiki, cheatsheet, OCR). Confidence = how much verifiable support exists. Fast trust decision: accept, verify, or provide lockable context.

KAIOKEN - live register classifier. Every human turn is macro-labelled (working / casual / personal) with subsignal tags (playful / friction / distress_hint / etc.) before the model fires. A validated, global decision tree - not LoRA or vibes - assigns tone constraints from classifier output. Validated against 1,536 adversarial probe executions, 3/3 pass required per probe. End result: your model stops being a sycophant. It might tell you to go to bed. It won't tell you "you're absolutely right!" when what you really need is a kick in the arse.

Cheatsheets - drop a JSONL file, terms auto-match on every turn, verified facts injected before generation. Miss on an unknown term? Routes to >>wiki instead of letting the model guess. Source: Cheatsheets in the footer. Your knowledge, your stack, zero confabulation on your own specs.

Vodka - deterministic memory pipeline. !! store is SHA-addressed and verbatim. ?? recall retrieves deterministically, bypasses model entirely. What you said is what comes back - no LLM smoothing, no creative reinterpretation. Without this? Your model confidently tells you your server IP is 127.0.0.1. Ask me how I know.

>>flush / !!nuke - flush context or nuke it from orbit. Your data, your call, one command. "Delete my data" is a keystroke, not a support ticket.

>>scratch - paste any text, ask questions grounded only to that text. Lossless, no summarisation. Model cannot drift outside it. Want it to use multiple locked sources? You can.

>>summ and >>lock - deterministic extractive summarisation (pure Python, no LLM) + single-source grounding. Missing support → explicit "not found" label, not silent fallback.

##mentats - Vault-only deep retrieval. Thinker drafts from Vault facts, Critic (different model family) hunts violations, hallucinated content is deleted - never replaced with more hallucination, Thinker consolidates. No evidence to support claim? No answer. Gap explicitly stated.

Deterministic sidecars - >>wiki, >>weather, >>exchange, >>calc, >>define, >>vision/>>ocr. If a sidecar can do it, it does it deterministically.

Role orchestration - thinker, critic, vision, coder, judge - different families for error diversity. Swap any role in one line of config.

Personality Modes - Serious (default), Fun, Fun Rewrite, Raw passthrough. Model updates its snark and sarcasm based on how you talk to it.
Yes, TARS sliders. Style changes delivery, not evidence contracts.

So, wait...are you saying you solved LLM hallucinations?

No. I did something much more evil. I made it impossible for the LLM to bullshit quietly. I made hallucinations...unpalatable, so the model would rather say "shit, I don't know the answer. Please stop hurting me."

To which I say...no.

Wrong still happens (though much less often), and when it does, it comes with a source label, a confidence rating, and an audit trail.

TL;DR: I made "I don't know" a first-class output.

"In God We Trust; All others bring data." - Deming

Runs on:

A potato. I run this on my Lenovo P330 Tiny with 4GB VRAM and 640 CUDA cores; if it runs here, it runs on yours.

pip install git+https://codeberg.org/BobbyLLM/llama-conductor.git
python -m llama_conductor.launch_stack up --config llama_conductor/router_config.yaml

Full docs: FAQ | Quickstart

License: AGPL-3.0. Corps who use it, contribute back.

P.S.: The whole stack runs on llama.cpp alone. I built a shim that patches the llama.cpp WebUI to route API calls through llama-conductor - one backend, one frontend, zero extra moving parts. Desktop or LAN. That's it.

PPS.: I even made a Firefox extension for it. Gives you 'summarize', 'translate', 'analyse sentiment' and 'copy text to chat'. Doesn't send anything to the cloud AT ALL (it's just HTML files folded into a Firefox XPI).

"The first principle is that you must not fool yourself - and you are the easiest person to fool." - Feynman

PPPS: A meat popsicle wrote this. Evidence - bobbyllm.github.io/llama-condu…

Codeberg: codeberg.org/BobbyLLM/llama-co…

GitHub: github.com/BobbyLLM/llama-cond…

‘A different set of rules’: thermal drone footage shows Musk’s AI power plant flouting clean air regulations

Images confirm xAI is continuing to defy EPA regulations in Mississippi to power its flagship datacenters

^{Guardian staff reporter (The Guardian)}

SuspciousCarrot78

2026-01-22 12:41:57

I'm tired of LLM bullshitting. So I fixed it.

Hello!

As a handsome local AI enjoyer™ you’ve probably noticed one of the big flaws with LLMs:

It lies. Confidently. ALL THE TIME.

(Technically, it “bullshits” - link.springer.com/article/10.1…

I’m autistic and extremely allergic to vibes-based tooling, so … I built a thing. Maybe it’s useful to you too.

The thing: llama-conductor

llama-conductor is a router that sits between your frontend (OWUI / SillyTavern / LibreChat / etc) and your backend (llama.cpp + llama-swap, or any OpenAI-compatible endpoint). Local-first (because fuck big AI), but it should talk to anything OpenAI-compatible if you point it there (note: experimental so YMMV).

I tried to make a glass-box that makes the stack behave like a deterministic system, instead of a drunk telling a story about the fish that got away.

TL;DR: “In God we trust. All others must bring data.”

Three examples:

1) KB mechanics that don’t suck (1990s engineering: markdown, JSON, checksums)

You keep “knowledge” as dumb folders on disk. Drop docs (.txt, .md, .pdf) in them. Then:

• >>attach <kb> — attaches a KB folder
• >>summ new — generates SUMM_*.md files with SHA-256 provenance baked in
• `>> moves the original to a sub-folder

Now, when you ask something like:

“yo, what did the Commodore C64 retail for in 1982?”

…it answers from the attached KBs only. If the fact isn’t there, it tells you - explicitly - instead of winging it. Eg:

The provided facts state the Commodore 64 launched at $595 and was reduced to $250, but do not specify a 1982 retail price. The Amiga’s pricing and timeline are also not detailed in the given facts.

Missing information includes the exact 1982 retail price for Commodore’s product line and which specific model(s) were sold then. The answer assumes the C64 is the intended product but cannot confirm this from the facts.

Confidence: medium | Source: Mixed

No vibes. No “well probably…”. Just: here’s what’s in your docs, here’s what’s missing, don't GIGO yourself into stupid.

And when you’re happy with your summaries, you can:

• >>move to vault — promote those SUMMs into Qdrant for the heavy mode.

2) Mentats: proof-or-refusal mode (Vault-only)

Mentats is the “deep think” pipeline against your curated sources. It’s enforced isolation:

• no chat history
• no filesystem KBs
• no Vodka
• Vault-only grounding (Qdrant)

It runs triple-pass (thinker → critic → thinker). It’s slow on purpose. You can audit it. And if the Vault has nothing relevant? It refuses and tells you to go pound sand:

FINAL_ANSWER:
The provided facts do not contain information about the Acorn computer or its 1995 sale price.

Sources: Vault
FACTS_USED: NONE
[ZARDOZ HATH SPOKEN]

Also yes, it writes a mentats_debug.log, because of course it does. Go look at it any time you want.

The flow is basically: Attach KBs → SUMM → Move to Vault → Mentats. No mystery meat. No “trust me bro, embeddings.”

3) Vodka: deterministic memory on a potato budget

Local LLMs have two classic problems: goldfish memory + context bloat that murders your VRAM.

Vodka fixes both without extra model compute. (Yes, I used the power of JSON files to hack the planet instead of buying more VRAM from NVIDIA).

• !! stores facts verbatim (JSON on disk)
• ?? recalls them verbatim (TTL + touch limits so memory doesn’t become landfill)
• CTC (Cut The Crap) hard-caps context (last N messages + char cap) so you don’t get VRAM spikes after 400 messages

So instead of:

“Remember my server is 203.0.113.42” → “Got it!” → [100 msgs later] → “127.0.0.1 🥰”

you get:

!! my server is 203.0.113.42
?? server ip → 203.0.113.42 (with TTL/touch metadata)

And because context stays bounded: stable KV cache, stable speed, your potato PC stops crying.

---

There’s more (a lot more) in the README, but I’ve already over-autism’ed this post.

TL;DR:

If you want your local LLM to shut up when it doesn’t know and show receipts when it does, come poke it:

• Primary (Codeberg): codeberg.org/BobbyLLM/llama-co…
• Mirror (GitHub): github.com/BobbyLLM/llama-cond…

PS: Sorry about the AI slop image. I can't draw for shit.

PPS: A human with ASD wrote this using Notepad++. If it the formatting is weird, now you know why.

llama-conductor

Route workflows, not models. Glass-box, not black-box. Squash LLM nonsense.

^Codeberg.org

cross-posted from: lemmy.ml/post/45061709

There are at least a dozen people spending at least several hours attacking GrapheneOS across platforms on a daily basis. It's a very strange situation. How do these people have so much time and dedication to keep making posts across platforms attacking us? It's relentless.

Every day, dozens of new accounts join our chat rooms to spread the same fabrications about GrapheneOS including via direct messages.

On Hacker News, one of the accounts making personal attacks based on fabrications in most threads about GrapheneOS has been doing it for 8 years.

Y Combinator has a financial stake in numerous surveillance and exploit development companies. Hacker News is a platform they own and the moderators on it have permitted years of vile harassment towards our team which they'd normally remove if others were targeted.

Hacker News mods micromanage it enough to repeatedly ask us not to reuse a bit of text across our comments. Meanwhile, they do nothing about disgusting personal attacks and harassment content consistently being spread in threads about GrapheneOS on their heavily moderated site.

The largest privacy community on Reddit /r/privacy bans any discussion or mentions of GrapheneOS. A bot automatically removes any post mentioning GrapheneOS they'll very actively ban people who evade their filters. The mods of the subreddit misrepresent this as something we want.

Many privacy subreddits have mods who are hostile towards GrapheneOS. We were banned from posting on /r/Android for multiple years. The mod who banned us said our official project account on Reddit was ban evading because they once unjustifiably banned one of our team members.

On Wikipedia, a company attacking GrapheneOS project made years of edits to the site pushing false narratives about us. They cited articles based on their own press releases. Other content was made paraphrasing Wikipedia which ended up being cited by it. It continues to this day.

Articles about GrapheneOS on most platforms often have comments engaging in baseless personal attacks towards our team, linking to harassment content and making many clearly inaccurate claims about it. We've found chat rooms coordinating this including attacks on the X platform.

Privacy projects are more vulnerable to these attacks because the userbase and supporters largely avoid social media and other platforms where it happens. Many people believe what they read on social media if it isn't countered and it builds echo chambers hostile to GrapheneOS.

Many people think these must be state sponsored attacks. However, our experience is these attacks are primarily orchestrated by companies selling dubious products marketed as private and secure. We did get targeted by state sponsored smear campaigns in France and Spain though.