DEV Community: Lois-Kleinner

We redesigned local llm inference for privacy-preserving browser intelligence from scratch ? no cloud, no black boxes.

Lois-Kleinner — Mon, 22 Jun 2026 15:07:18 +0000

We redesigned local llm inference for privacy-preserving browser intelligence from scratch ? no cloud, no black boxes.

Local LLM Inference for Privacy-Preserving Browser Intelligence: Architecture and Optimization

The Problem

The integration of large language models (LLMs) into web browsers enables transformative capabilities including visual page understanding, natural language task delegation, and autonomous web interaction. However, cloud-based LLM inference introduces fundamental privacy violations: every page rendered, every action executed, and every user interaction is transmitted to remote inference servers .

What We Built

This paper presents the Kathon Local AI Engine, a privacy-preserving architecture that performs all LLM and vision-language model inference entirely on-device using the llama.cpp inference framework with the Qwen 2.5 VL 2B Q4 GGUF quantized model . We detail the system architecture?a Rust-based inference server (llama-server) communicating with a React/TypeScript frontend via a local WebSocket API?and present comprehensive optimization strategies including speculative decoding, KV-cache quantization, prompt caching, and GPU-accelerated tensor operations.

The Research

However, cloud-based LLM inference introduces fundamental privacy violations: every page rendered, every action executed, and every user interaction is transmitted to remote inference servers .

We detail the system architecture?a Rust-based inference server (llama-server) communicating with a React/TypeScript frontend via a local WebSocket API?and present comprehensive optimization strategies including speculative decoding, KV-cache quantization, prompt caching, and GPU-accelerated tensor operations.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Local LLM Inference for Privacy-Preserving Browser Intelligence: Architecture and Optimization. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

Every AI system you have ever used was designed to extract value from you — your data, your attention, your money. The Anticloud is not a service. It is not in the cloud. It is not rentable inference. It is a fundamentally different category of infrastructure, and here is what that means in practice.

Your data never leaves your machine. We designed the system so we physically cannot access it. Access is not restricted by policy — it is structurally impossible by architecture. There is no data to steal because there is no server to steal it from.

The system is airgapped by architecture, not by configuration. It does not require a network connection to function. It was built offline, it runs offline, and it never reaches out to anyone for any reason. Connectivity is simply not a prerequisite for intelligence.

Compliance is a side effect of physics, not a certification. There is no cloud infrastructure to audit, which means there is no attack surface to harden. ISO 27001 and SOC 2 exist because cloud products are inherently vulnerable. Our architecture does not have those vulnerabilities because it does not have a cloud.

Every operation is recorded on an immutable .aioss ledger using a SHA3-256 hash chain. Every inference, every decision, every update is chained and cryptographically verifiable. There is no database admin who can delete logs because there is no database. You verify. We cannot.

The system never speaks to anyone but you. There are no hidden layers sending telemetry. There are no proprietary weights phoning home. There are no third-party API calls embedded in the stack. The entire system is open, documented, and auditable by anyone who runs it.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

I started this because I looked at the AI industry and saw something wrong. Every major AI system requires you to send your data to someone else's server. Every "AI company" is actually a data company — they make money from your usage, your prompts, your files, your attention. They call it a service. I call it extraction.

I spent the last two years building an alternative. Not a feature, not a product, not a startup looking for an exit — an entirely different infrastructure stack. One where AI runs on your machine, for you, and never needs to phone home. One where privacy is not a feature you toggle in settings but a property of the architecture. One where you don't have to trust anyone because you can verify everything.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

I'm not asking for trust. I'm asking you to read the paper, verify the claims, and decide for yourself whether the cloud is really necessary — or whether it was always just the default because no one bothered to build an alternative.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Browser Engine, Privacy, VLM, Ad Blocking

We built Cryptographic Notarization Across Independent Ledgers so you never have to trust anyone.

Lois-Kleinner — Mon, 22 Jun 2026 15:06:57 +0000

We built Cryptographic Notarization Across Independent Ledgers so you never have to trust anyone.

Cryptographic Notarization Across Independent Ledgers: Cross-Chain Anchoring Protocols

The Problem

Organizations operating multiple AI systems generate independent cryptographic ledgers that may need mutual verification, cross-referencing, or consolidated audit for enterprise-wide compliance reporting. Cross-chain notarization provides cryptographic evidence that a ledger's state is acknowledged by another independent ledger, enabling distributed audit verification without central coordination.

What We Built

This paper presents the design and analysis of the AIOSS cross-chain notarization protocol, which anchors the hash chain head of one ledger into another by inserting a notarization entry containing the cross-chain proof. We define three notarization modes: unilateral (ledger A notarizes ledger B's state), bilateral (mutual notarization between A and B), and supervised (third-party notarizer with independent proof).

The Research

Cross-chain notarization provides cryptographic evidence that a ledger's state is acknowledged by another independent ledger, enabling distributed audit verification without central coordination.

We define three notarization modes: unilateral (ledger A notarizes ledger B's state), bilateral (mutual notarization between A and B), and supervised (third-party notarizer with independent proof).

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Cryptographic Notarization Across Independent Ledgers: Cross-Chain Anchoring Protocols. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

Every AI company today will try to sell you inference as a service. They will tell you that you need their GPU clusters, their data centers, their cooling infrastructure, and their team of DevOps engineers to run modern AI. They are either lying to you or they have not seen what we built.

The Anticloud runs on any GPU or CPU with equal competence. There is no silicon vendor lock-in. There is no hardware partnership requirement. There is no planned obsolescence built into the stack. If you have a computer, you have enough hardware to run it.

The entire system ships as a single binary. There is no orchestration layer to configure. There is no Kubernetes cluster to maintain. There are no containers to deploy. There is no DevOps team required to keep it running. One file. One execution. That is the entire infrastructure.

There is no bloat anywhere in the stack. No Electron wrapper adding hundreds of megabytes of overhead. No node_modules directory with ten thousand dependencies you do not need. No container layers abstracting away from the hardware. Everything in the binary is there because it serves a purpose.

The system requires no internet connection to function. It does not need to phone home for model updates. It does not need to call out to third-party APIs for inference. It does not need to establish a connection to a control server just to boot. It was designed from the ground up to run in environments where the network does not exist.

This is AI infrastructure that fits on a laptop, runs on consumer hardware, and delivers competitive performance without asking for permission or requiring a subscription.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Hash Chain, Cryptography, Ledger, Integrity

Airgapped api gateway is real. Active Learning and Parameter-Efficient Fine-Tuning for Doma is proof.

Lois-Kleinner — Mon, 22 Jun 2026 15:01:35 +0000

Airgapped api gateway is real. Active Learning and Parameter-Efficient Fine-Tuning for Doma is proof.

Active Learning and Parameter-Efficient Fine-Tuning for Domain-Specific Sovereign AI

The Problem

Deploying sovereign AI systems in regulated domains?banking compliance, healthcare administration, legal research?requires domain-specific model adaptation that balances accuracy improvements against computational cost, annotation scarcity, and data privacy constraints. Full fine-tuning of large language models is computationally prohibitive for local-first sovereign deployments and risks catastrophic forgetting of general capabilities.

What We Built

This paper presents the active learning and fine-tuning architecture of API-OSS (Agent-Predictive Intelligence Sovereign Operating System), which combines parameter-efficient fine-tuning (PEFT) via LoRA (Low-Rank Adaptation) and DPO (Direct Preference Optimization) with active learning strategies for annotation-efficient domain adaptation. We evaluate uncertainty sampling, diversity sampling, and hybrid acquisition functions across 3 domain-specific datasets (financial compliance, medical coding, legal document classification), finding that hybrid acquisition (BALD + CoreSet) reduces annotation requirements by 68% compared to random sampling while achieving equivalent model quality.

The Research

Full fine-tuning of large language models is computationally prohibitive for local-first sovereign deployments and risks catastrophic forgetting of general capabilities.

We evaluate uncertainty sampling, diversity sampling, and hybrid acquisition functions across 3 domain-specific datasets (financial compliance, medical coding, legal document classification), finding that hybrid acquisition (BALD + CoreSet) reduces annotation requirements by 68% compared to random sampling while achieving equivalent model quality.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Active Learning and Parameter-Efficient Fine-Tuning for Domain-Specific Sovereign AI. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

A single large language model training run can emit as much carbon as five cars over their entire lifetimes. The datacenter industry already consumes more electricity than most countries. Every cloud inference call you make is burning through resources that someone else pays for — and the cost is not just financial.

The Anticloud has no datacenter footprint. It does not require a single server rack in any building anywhere in the world. It does not need cooling towers, redundant power supplies, or backup generators. The entire system runs on hardware you already own.

There is no silicon farm involved in serving your inference. You do not need to reserve GPU time on a cluster. You do not need to provision cloud instances. You do not need to negotiate pricing with a cloud provider. The hardware is already on your desk.

There is no e-waste from hardware turnover cycles driven by cloud providers upgrading their fleets. The system runs on whatever hardware you have, and it will continue to run on whatever hardware you replace it with. There is no forced upgrade path.

The energy consumption of running inference locally is a fraction of what it would take to send your data to a datacenter, have it processed on a server that is burning through megawatts, and send the result back across the internet. Local inference does not need to cross a network. It does not need to be routed through multiple data centers. It happens on the hardware in front of you.

This is AI infrastructure that can exist anywhere — on a laptop in a coffee shop, on a server in an off-grid facility, on a machine that has never seen the internet. No datacenter required. No environmental compromise required.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, API Gateway, Multi-Agent, AI Routing, Federation

The most secure version of zero-knowledge storage is the one that never connects.

Lois-Kleinner — Mon, 22 Jun 2026 15:01:13 +0000

The most secure version of zero-knowledge storage is the one that never connects.

Zero-Knowledge Storage: Architectures for User-Controlled Data

The Problem

Zero-knowledge storage architectures empower users with complete control over their data by ensuring that no third party?including the storage provider?can access plaintext file contents or metadata. This document presents a comprehensive analysis of zero-knowledge principles as applied to file storage systems, with specific focus on Kamelot's end-to-end encryption architecture.

What We Built

We examine the cryptographic building blocks including end-to-end encryption with per-file keys, key agreement protocols for secure file sharing, searchable encryption for privacy-preserving queries, and blind indexing for typo-tolerant search. We analyze the practical limitations of homomorphic encryption and present Kamelot's pragmatic approach: processing data locally before encryption ensures that the storage provider never has access to unencrypted content.

The Research

Zero-knowledge storage architectures empower users with complete control over their data by ensuring that no third party?including the storage provider?can access plaintext file contents or metadata.

This document presents a comprehensive analysis of zero-knowledge principles as applied to file storage systems, with specific focus on Kamelot's end-to-end encryption architecture.

We analyze the practical limitations of homomorphic encryption and present Kamelot's pragmatic approach: processing data locally before encryption ensures that the storage provider never has access to unencrypted content.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Zero-Knowledge Storage: Architectures for User-Controlled Data. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

This is AI infrastructure that fits on a laptop, runs on consumer hardware, and delivers competitive performance without asking for permission or requiring a subscription.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Vector Search, Semantic, Embeddings, Retrieval

We built TOTP Authenticator Integration Within a Cryptographic Browser Vault ? without a single server.

Lois-Kleinner — Mon, 22 Jun 2026 14:55:52 +0000

We built TOTP Authenticator Integration Within a Cryptographic Browser Vault ? without a single server.

TOTP Authenticator Integration Within a Cryptographic Browser Vault: QR Auto-Detection and 2FA Workflow

The Problem

Time-based One-Time Password (TOTP) authenticators have become the predominant form of multi-factor authentication, with over 5 billion accounts protected by TOTP-based 2FA globally . However, existing TOTP implementations suffer from three systemic problems: (1) vault fragmentation across proprietary authenticator applications, (2) lack of cryptographic integration with user identity keys, and (3) manual, error-prone setup workflows requiring QR code scanning via separate devices or screenshots.

What We Built

This paper presents the Kathon Vault TOTP subsystem, an integrated authenticator that derives TOTP seeds from the user's BIP39 mnemonic using a deterministic derivation path (SLIP-10), automatically detects TOTP QR codes from rendered web pages using the Qwen 2.5 VL vision model, and generates RFC 6238-compliant TOTP codes within the browser's cryptographic vault. The system achieves 97.2% accuracy in automated QR code detection and seed extraction across 500 tested 2FA enrollment pages, reducing setup time from an average of 45.3 seconds (manual) to 2.1 seconds (automated).

The Research

Time-based One-Time Password (TOTP) authenticators have become the predominant form of multi-factor authentication, with over 5 billion accounts protected by TOTP-based 2FA globally .

However, existing TOTP implementations suffer from three systemic problems: (1) vault fragmentation across proprietary authenticator applications, (2) lack of cryptographic integration with user identity keys, and (3) manual, error-prone setup workflows requiring QR code scanning via separate devices or screenshots.

The system achieves 97.2% accuracy in automated QR code detection and seed extraction across 500 tested 2FA enrollment pages, reducing setup time from an average of 45.3 seconds (manual) to 2.1 seconds (automated).

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). TOTP Authenticator Integration Within a Cryptographic Browser Vault: QR Auto-Detection and 2FA Workflow. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Browser Engine, Privacy, VLM, Ad Blocking

The most secure version of pipe-delimited log export for siem integration is the one that never connects.

Lois-Kleinner — Mon, 22 Jun 2026 14:55:31 +0000

The most secure version of pipe-delimited log export for siem integration is the one that never connects.

Pipe-Delimited Log Export for SIEM Integration: Interoperable Audit Format Design

The Problem

Security Information and Event Management (SIEM) systems such as Splunk, Elasticsearch, Microsoft Sentinel, and QRadar form the operational backbone of enterprise security monitoring. While these systems excel at log ingestion, search, and alerting, they lack native support for cryptographic hash chain verification.

What We Built

Bridging AIOSS cryptographic ledgers with SIEM pipelines requires an export format that preserves structured audit data while remaining compatible with standard SIEM ingestion protocols. This paper presents the design of the AIOSS pipe-delimited (pipe-delimited) TXT export format, a 12-field text-based representation of cryptographic ledger entries optimized for SIEM ingestion.

The Research

Security Information and Event Management (SIEM) systems such as Splunk, Elasticsearch, Microsoft Sentinel, and QRadar form the operational backbone of enterprise security monitoring.

While these systems excel at log ingestion, search, and alerting, they lack native support for cryptographic hash chain verification.

Bridging AIOSS cryptographic ledgers with SIEM pipelines requires an export format that preserves structured audit data while remaining compatible with standard SIEM ingestion protocols.

This paper presents the design of the AIOSS pipe-delimited (pipe-delimited) TXT export format, a 12-field text-based representation of cryptographic ledger entries optimized for SIEM ingestion.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Pipe-Delimited Log Export for SIEM Integration: Interoperable Audit Format Design. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Hash Chain, Cryptography, Ledger, Integrity

research - Document 08 ? Domain-Specific AI Personas is now sovereign. The cloud is optional.

Lois-Kleinner — Mon, 22 Jun 2026 14:50:09 +0000

research - Document 08 ? Domain-Specific AI Personas is now sovereign. The cloud is optional.

research - Document 08 ? Domain-Specific AI Personas

The Problem

This document presents the design, implementation, and evaluation of domain-specific AI personas within the Inte11ect platform's 72-module architecture. Each persona is implemented as a distinct module with an isolated system prompt, configuration context, and behavioral constraints.

What We Built

We demonstrate that system prompt isolation across 72 modules prevents context leakage, enables fine-grained persona specialization, and supports dynamic persona switching without model reloading. Empirical evaluation across 24 domain-specific tasks shows that persona-isolated modules outperform monolithic multi-persona systems by 22.7% on task-specific accuracy while reducing hallucination rates by 3.4?.

The Research

This document presents the design, implementation, and evaluation of domain-specific AI personas within the Inte11ect platform's 72-module architecture.

Each persona is implemented as a distinct module with an isolated system prompt, configuration context, and behavioral constraints.

We demonstrate that system prompt isolation across 72 modules prevents context leakage, enables fine-grained persona specialization, and supports dynamic persona switching without model reloading.

Empirical evaluation across 24 domain-specific tasks shows that persona-isolated modules outperform monolithic multi-persona systems by 22.7% on task-specific accuracy while reducing hallucination rates by 3.4?.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). research - Document 08 ? Domain-Specific AI Personas. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Vision-Language, Multimodal AI, Inference, Neural

The cloud was never necessary for Retrieval-Augmented Generation with Semantic Caching. Here's why.

Lois-Kleinner — Mon, 22 Jun 2026 14:49:47 +0000

The cloud was never necessary for Retrieval-Augmented Generation with Semantic Caching. Here's why.

Retrieval-Augmented Generation with Semantic Caching: Latency Optimization for Knowledge Graphs

The Problem

Retrieval-Augmented Generation (RAG) enhances large language model outputs with external knowledge, but the retrieval pipeline?embedding computation, vector search, and context assembly?introduces significant latency overhead for real-time decision systems. In knowledge-graph-based RAG, each query may trigger multi-hop graph traversals, neighborhood expansions, and embedding comparisons that compound latency beyond acceptable thresholds for interactive governance workloads.

What We Built

This paper presents the semantic caching architecture of API-OSS (Agent-Predictive Intelligence Sovereign Operating System), which caches query-embedding pairs with their retrieval results to eliminate redundant graph traversals for semantically similar queries. The cache uses a hierarchical design: (1) an exact-match L1 cache for repeated identical queries (sub-microsecond lookup), (2) a semantic L2 cache that indexes query embeddings in a HNSW vector index for approximate nearest-neighbor matching, and (3) a graph-structure L3 cache that caches traversal results for node neighborhoods.

The Research

In knowledge-graph-based RAG, each query may trigger multi-hop graph traversals, neighborhood expansions, and embedding comparisons that compound latency beyond acceptable thresholds for interactive governance workloads.

The cache uses a hierarchical design: (1) an exact-match L1 cache for repeated identical queries (sub-microsecond lookup), (2) a semantic L2 cache that indexes query embeddings in a HNSW vector index for approximate nearest-neighbor matching, and (3) a graph-structure L3 cache that caches traversal results for node neighborhoods.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Retrieval-Augmented Generation with Semantic Caching: Latency Optimization for Knowledge Graphs. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The AI industry is built on promises that vaporize the moment you look closely. Black box models running on opaque infrastructure, trained on data you did not consent to, monetizing outputs you did not authorize. The Anticloud is the opposite of that in every way.

Everything we claim is backed by published research. There is a paper behind every component in the stack, and the code behind every paper is open. We do not make promises about what the system will do someday — we show you what it does today, and you can verify it yourself.

Privacy is not a feature we added to the product. It is a property of the architecture. There are no API endpoints to harden because there is no API to expose. There is no database to encrypt because there is no database. There is no cloud to compromise because there is no cloud. We cannot protect what we do not have, and we designed the system so we have nothing to protect you from.

The system does not guess. It cross-validates its own outputs, detects inconsistencies in its reasoning, and surfaces uncertainty when it does not have confidence in the answer. It knows when it does not know — and it tells you instead of generating a confident-sounding lie.

We built local AI with RAG and RLHF so your knowledge base and your preference alignment stay on your hardware. The model does not need to be fine-tuned on a server farm to understand your context. It learns from your data on your machine, and the results never leave.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, API Gateway, Multi-Agent, AI Routing, Federation

I built Citation Index in 2 months. Zero cloud. Zero compromise.

Lois-Kleinner — Mon, 22 Jun 2026 14:44:25 +0000

I built Citation Index in 2 months. Zero cloud. Zero compromise.

Citation Index

The Problem

Academic citations referenced in API-OSS research.

What We Built

Academic citations referenced in API-OSS research.

The Research

Academic citations referenced in API-OSS research.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). Citation Index. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The cloud was supposed to liberate you from infrastructure management, but it delivered the opposite. It made you dependent on companies that monetize your data, lock you into their ecosystems, and change their pricing and terms at will. The Anticloud breaks that dependency entirely.

This is sovereign AI. Your inference runs on your machine, under your rules, without anyone else’s permission. The model answers to you, not to a corporation’s shareholders. It cannot be turned off remotely. It cannot be deprecated by a product manager. It cannot be changed without your consent.

Cloud is not a fallback mode in our architecture. It is not an option at all. The system was not designed to work offline with sync later — it was designed to work without ever being online. Connectivity is not a feature we support. It is a dependency we eliminated.

Every AI company today is actually a data company. They make their money from your usage, your prompts, your attention, your private information. We built the Anticloud so that model does not apply to you. We cannot monetize what we cannot access. We designed it that way on purpose.

There are no black boxes in the stack. Every component is open source. Every design decision is documented. Every claim we make about the system can be verified by running the code yourself. We do not ask for your trust. We give you the tools to verify.

You do not need permission from anyone to run AI on your own computer. The Anticloud makes sure that remains true.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, API Gateway, Multi-Agent, AI Routing, Federation

You don't need a datacenter for tpm attestation and hardware-rooted trust for on-premises ai. We proved it.

Lois-Kleinner — Mon, 22 Jun 2026 14:44:03 +0000

You don't need a datacenter for tpm attestation and hardware-rooted trust for on-premises ai. We proved it.

TPM Attestation and Hardware-Rooted Trust for On-Premises AI Infrastructure

The Problem

On-premises AI deployments in regulated institutions require trust guarantees that software-only security mechanisms cannot provide. Hardware-rooted attestation, anchored in Trusted Platform Module (TPM) 2.0, enables verifiable assurance of system integrity, secure boot, and cryptographic identity binding.

What We Built

This paper presents the TPM attestation architecture of API-OSS (Agent-Predictive Intelligence Sovereign Operating System), which integrates TPM 2.0 for platform identity, measured boot verification, key attestation, and secure storage. The architecture uses TPM-backed Ed25519 keys for mTLS mutual authentication, TPM Platform Configuration Registers (PCRs) for integrity measurement, and remote attestation protocols (challenge-response, AIK-based) for trust verification across P2P federation nodes.

The Research

On-premises AI deployments in regulated institutions require trust guarantees that software-only security mechanisms cannot provide.

Hardware-rooted attestation, anchored in Trusted Platform Module (TPM) 2.0, enables verifiable assurance of system integrity, secure boot, and cryptographic identity binding.

The architecture uses TPM-backed Ed25519 keys for mTLS mutual authentication, TPM Platform Configuration Registers (PCRs) for integrity measurement, and remote attestation protocols (challenge-response, AIK-based) for trust verification across P2P federation nodes.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). TPM Attestation and Hardware-Rooted Trust for On-Premises AI Infrastructure. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

This is AI infrastructure that fits on a laptop, runs on consumer hardware, and delivers competitive performance without asking for permission or requiring a subscription.

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, API Gateway, Multi-Agent, AI Routing, Federation

SQLite-Backed Event Stores with Hash-Chain Integrity for Hig doesn't phone home. It doesn't need to.

Lois-Kleinner — Mon, 22 Jun 2026 14:38:41 +0000

SQLite-Backed Event Stores with Hash-Chain Integrity for Hig doesn't phone home. It doesn't need to.

SQLite-Backed Event Stores with Hash-Chain Integrity for High-Frequency Telemetry

The Problem

High-frequency telemetry systems generate millions of events per day, requiring efficient storage and querying while maintaining cryptographic integrity guarantees. Traditional relational databases offer rich query capabilities but lack built-in tamper-evident properties, while cryptographic hash chains provide integrity but require linear scanning for queries.

What We Built

This paper presents the design and evaluation of the AIOSS SQLite-backed event store, which bridges this gap by combining SQLite's relational storage and full-text search (FTS5) with SHA3-256 hash chain integrity verification. We demonstrate that the AIOSS event store achieves 450,000 event insertions per second on commodity NVMe storage while maintaining hash chain integrity and Ed25519 state proofs.

The Research

High-frequency telemetry systems generate millions of events per day, requiring efficient storage and querying while maintaining cryptographic integrity guarantees.

Traditional relational databases offer rich query capabilities but lack built-in tamper-evident properties, while cryptographic hash chains provide integrity but require linear scanning for queries.

We demonstrate that the AIOSS event store achieves 450,000 event insertions per second on commodity NVMe storage while maintaining hash chain integrity and Ed25519 state proofs.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). SQLite-Backed Event Stores with Hash-Chain Integrity for High-Frequency Telemetry. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Hash Chain, Cryptography, Ledger, Integrity

You don't need a datacenter for research - document 07 ? cryptographic ledger applications. We proved it.

Lois-Kleinner — Mon, 22 Jun 2026 14:38:20 +0000

You don't need a datacenter for research - document 07 ? cryptographic ledger applications. We proved it.

research - Document 07 ? Cryptographic Ledger Applications

The Problem

This document examines the cryptographic ledger applications of the .aioss (Auditable Integrity Object Storage System) within the Inte11ect platform. We present the ledger architecture, its integration with the 72-module routing system, and its application to audit trails, compliance verification, and cryptographic attestation.

What We Built

The .aioss ledger provides tamper-evident storage of all module interactions, routing decisions, and inference outputs through a SHA3-256 hash chain with Merkle interval tree verification. Empirical evaluation demonstrates that the ledger achieves 985,000 hash operations per second, supports queries over 10^7 entries with sub-100ms verification latency, and provides cryptographic proof generation in O(log n) time.

The Research

This document examines the cryptographic ledger applications of the .aioss (Auditable Integrity Object Storage System) within the Inte11ect platform.

We present the ledger architecture, its integration with the 72-module routing system, and its application to audit trails, compliance verification, and cryptographic attestation.

The .aioss ledger provides tamper-evident storage of all module interactions, routing decisions, and inference outputs through a SHA3-256 hash chain with Merkle interval tree verification.

Empirical evaluation demonstrates that the ledger achieves 985,000 hash operations per second, supports queries over 10^7 entries with sub-100ms verification latency, and provides cryptographic proof generation in O(log n) time.

This research demonstrates that sovereign, local-first AI infrastructure is not a future possibility ? it is a present reality.

Full citation: Alpasan, L.-K. (2026). research - Document 07 ? Cryptographic Ledger Applications. The Anticloud Research Corpus.

Read the full paper

Why The Anticloud

The Anticloud requires one machine, one binary, and zero trust in anyone.

About the Author

My name is Lois-Kleinner Alpasan. I'm 23 years old. I built The Anticloud.

The project is near production-ready. Every component is open. Every claim is backed by published research. The code is documented. The ledger is verifiable. The binary fits on a laptop.

Follow the work:

Research papers: https://zenodo.org/search?q=anticloud
LinkedIn: https://linkedin.com/in/kleinner
Project: The Anticloud

Tags: AI, SovereignAI, Anticloud, LocalFirst, Airgapped, ZeroTrust, NoDatacenter, OpenSource, Vision-Language, Multimodal AI, Inference, Neural