67 karma · joined August 15, 2026
The other issue is GitHub allowing 1000's of black hat open-source exploit malware swarm tools being forked.
The future internet is going to be perilous.
Within 12 hours, the analyst agents produced 2,308 candidate findings after deduplication. This stage cost $316,549 in API credits.
I think we will find the market is going to move into omni multi-modal models: text, moe, agentic tasks, voice, images, and video all in one LLM.
$90,000 to $105k on Google - Ad revenue is a nice touch.
Interesting: 9.7 million YouTube views are worth approx $12,125 to $29,100 USD in standard long-form ad revenue; you can make some spare cash whilst you run for office as well, pay the bills.
Extracted whitepapers, should keep you busy:
VL-JEPA: Joint Embedding Predictive Architecture for Vision-language [2512.10942v1] A Multi-Memory Segment System for Generating [2508.15294v3] Adversarial Cues in Decision Models Used [2610.11436v1] AGENTIC BACKDOORS VIA PRETRAINING POISONING [2610.37741v1] Agentic Memory Learning Unified Long-Term [2601.01885v2] Agentic Semi-Structured Memory [2604.14362v1] Agents That Know Too Much [2606.26627v1] AI Agents Enable Adaptive [2606.03811v1] AI Agents That Matter [2407.01502v1] AI Assistants Cloud Versus Local LLM [2604.18566v2] AMA-Bench Evaluating Long-Horizon Memory [2602.22769v2] A-Mem Agentic Memory for LLM Agents [2502.12110v11] APEX-MEM Agentic Semi-Structured Memory [2604.14362v1] ASK ONLY WHEN NEEDED [2604.20572v2] Attention Is All You Need [1706.03762v7] Attention Is Not What You Need [2512.19428v1] Benchmarking Multi-Agent LLM [2603.22651v1] Beyond Nodes vs. Edges [2604.14685v1] Beyond Static Summarization [2601.04463v1] Calibrated Decision Models for Autonomous [2609.28940v1] Character-and-Scene Episodic Memory for Agents [2602.06051v3] CLASP Training-Free LLM-Assisted Source Code Watermarking [2510.11251v2] Cognitive Architectures for Language Agents [2309.02427v3] COMPUTATIONAL COPYRIGHT [2312.06646v5] Constitutional AI [2212.08073v1] Creative Problem Solving in Artificially Intelligent Agents [2204.10358v1] CyberGym [2506.02548v3] Deep image search [2602.10809v1] Developing Surrogates [2602.21588v1] Diffusion-Based Neural Network Weights Generation [2402.18153v2] Dynamic LLM-Powered Agent Network [2310.02170v2] Dynamic Logit-Level Gating [2606.04378v1] EternalMath [2601.01400v1] EverMemOS [2601.02163v2] EvoMemBench Benchmarking Agent Memory [2605.18421v1] ExploitBench [2605.14153v1] FADEMEM BIOLOGICALLY-INSPIRED [2601.18642v2] Finance-Grounded Optimization [2509.04541v2] Formal Algorithms for Transformers [2207.09238v1] Geogalactica [2401.00434v2] HAGE - Harnessing Agentic Memory [2605.09942v1] HaluMem [2511.03506v1] Harnessing the Universal Geometry of Embeddings [2505.12540v4] HIPPOCAMPUS [2602.13594v1] hipporag [2405.14831v3] Hybrid Reasoning Agents [2610.18861v1] Hyper agents [2603.19461v1] HyperMem [2604.08256v2] Interactive Simulacra of Human Behavior [2304.03442v2] Is Complex Agentic Memory Always Needed [2610.30755v1] Language Modeling Is Compression [2309.10668v2] Learn from your own latents [2605.27734v1] Learning on the Job [2607.22157v1] Learning to Memorize at Test Time [2501.00663v1] Learning to Rewrite Tool Descriptions [2602.20426v2] LLM Agents Already Know When to Call Tools [2605.09252v2] LLM-Enabled Frequency-Aware [2602.19522v1] LONGMEMEVAL [2410.10813v2] MAGMA A Multi-Graph based [2601.03236v2] MemGround [2604.14158v1] Memory in the Age of AI Agents [2512.13564v2] Memory Tax [2610.34073v1] Memp - Exploring Agent Procedural Memory [2508.06433v4] Mitigating Provenance-Role [2605.25869v1] MOSS Self-Evolution through Source-Level Rewriting [2605.22794v2] Multi-task DAG Learning [2306.02831v1] Natural Language conductor [2512.04388v5] NeuroGen - Neural Network Parameter Generation [2505.12470v2] Next-Latent Prediction Transformers Learn Compact World Models [2511.05963v4] Novel Memory Forgetting Techniques [2604.02280v1] OMNI-LEAK Orchestrator Multi-Agen [2602.13477v2] On Understanding Extremely Long-Term Video with Adaptive [2411.12593v3] Opal Private Memory [2604.02522v1] Parameter Prediction for Unseen Deep Architectures [2110.13100v1] Prompt Optimization [2602.18449v1] PromptMark [2606.20835v1] RAG-MCP Mitigating Prompt Bloat [2505.03275v1] RAIN-MERGING [2602.22538v1] Recursive Self-Improvement through Multi-Agent [2610.12176v1] Reflection-Based Memory [2506.02158v1] Refusal in Language Models Is Mediated by a Single Direction [2406.11717v3] REMEM REASONING WITH EPISODIC MEMORY [2602.13530v3] Remember When It Matters [2607.08716v1] Remembering More, Risking More [2605.17830v1] Sakana Fugu Technical Report [2606.21228v1] SalesRLAgent [2503.23303v1] Scaling Long-Form Story Generation [2609.35759v1] Search-R1- Training LLM [2503.09516v5] Secure Semantic Communications [2602.22134v2] Selective Memory for Artificial Intelligence [2603.15994v1] Skill.state [2608.26263v2] Smarter Together [2511.08301v1] SmartSearch [2603.15599v1] SOLVING A MILLION-STEP LLM TASK [2511.09030v1] Storage Is Not Memory [2605.04897v1] StructMem Structured Memory [2604.21748v1] Structured Belief State and the First Precision [2605.11325v2] SuperLocalMemory V3.3 [2604.04514v1] SWE-MeM Learning Adaptive Memory [2606.28434v1] Synthius-Mem Brain-Inspired Hallucination [2604.11563v1] The AI Scientist-v2 [2504.08066v1] The Bitter Lesson of Tool Calling [2608.06370v1] The Customer Is the Builder [2610.30307v1] The Gradient of Generative AI Release [2302.04844v1] The Little Book of Generative AI Foundations [2605.29713v2] The Malicious Use [1802.07228v2] The Memory Wars [2508.05867v1] Thinking Fast and Slow in AI [2110.01834v1] Time algo [2610.00577v1] Towards trustworthy agentic AI [2605.23989v1] Transformers learn in-context by gradient descent [2212.07677v2] TReMu Towards Neuro-Symbolic Temporal Reasoning [2502.01630v2] TRINITY AN EVOLVED LLM COORDINATOR [2512.04695v3] VERBALIZED SAMPLING [2510.01171v3] Verifiable Agent-to-Agent [2602.02395v1] Verified Multi-Agent Orchestration [2603.11445v2] WEBCOACH - A MODEL [2511.12997v1] XGrammar-2 Dynamic and Efficient Structured Generation [2601.04426v4] Your Agent’s Memories Are Not Its Own [2607.05029v1] Zep A Temporal Knowledge Graph Architecture [2501.13956v1]
8Gb of whitepapers/books...
We did a combo books/video HN remix a few days ago: https://vektormemory.com/blog/v2-0-4-hacker-news-books-video...
They are slicing, probing, and running linear algebra on the tensors, observing the output in the console, then writing the edited weights back to disk, and stitching it all back up.
Johnny Mnemonic level
It does need a new-to-old sort though, which is easy to add in.
If you watched all of these videos, you would be the smartest, most annoying person ever. Lol
If you have a billion users and 90% + are freeloaders, you need social media style ads to support growth, which no one likes.
Plus they are all out of data training sets, they are buying zombie companies for their data.
There are dozens of distillated clones, which are getting better every release.
The only moat is expensive data centre builds with on-loan Nvidia inference and the Chinese embargo fears.
Good luck out there.
He is still in China living with his girlfriend.
Miami, Florida – A 17-count indictment was unsealed in federal court in Miami, Florida charging Paul E. Vernon, 48, the founder, operator, and Chief Executive Officer (CEO) of Project Investors Inc., doing business as Cryptsy
https://www.justice.gov/usao-sdfl/pr/ceo-major-online-crypto...
But it would have been easier to relocate to the border in Tahoe than fill out mountains of paperwork; he lives on an estate in a Maui bunker anyway, not spoilt for housing options...
Not very good with building technology; he lost 80 billion on a nonexistent metaverse fail. Is that now a tax loss?
Art vs. minimalism in design
Icons are today's form of hieroglyphs for communication?
Home Trending Hacker News Hits Archive Stats Tags Links
https://medium.com/@vektormemory/the-si-wealth-transfer-is-b...
Essentially another layer and commodity that users will pay more for, as humans value speed and volume over authenticity.
Same reason almost no one writes with pens or typewriters any longer, as they are niche artifacts.
Can't wait for the open-source release of Google on GitHub.
Created a long rant with some solutions and free tool Vorwatch: https://vektormemory.com/opensource
Unpaywalled article: https://medium.com/@vektormemory/bots-lots-of-them-5a1e35be2...
I always wanted to build a mini CM-2 supercomputer with the red lights.
I started something similar as a meme game, but I'm just too busy to keep working on it: Data centre slumlord - https://binary-mirror.itch.io/data-centre-slumlord
OK, I'm jealous... Lol
Access is currently limited. We'll contact you if early access becomes available.
Your talking about androids...
Replicant Nexus 6: a basic pleasure model intended for military personnel.
I see where this is going, Silicon Valley nerds. Lol
AI advising how human meat proxies can survive in an AGI-slop world:
1) Lock down your own stack (1–3 days) Task: Harden your personal and business infrastructure against agentic attacks. Why now: Agents are becoming superhuman at breaking in/out of systems; the first victims are poorly secured devs/founders.
Do this:
Enforce passkeys + hardware 2FA everywhere; rotate secrets; use short‑lived credentials.
Isolate dev/stage/prod; least‑privilege API keys; audit MCP/tools your agents can call.
Add immutable logs and approval gates for any agent action that touches money, data exports, or production.
Profit link: You avoid catastrophic loss and can credibly sell “agent‑safe” setups to others.
2) Turn one expensive workflow into a measured ROI agent (1–2 weeks) Task: Pick a single, costly, repetitive process (yours or a client’s) and instrument it end‑to‑end before automating.
Why now: Buyers pay for calculable ROI, not “AI magic.” Vertical, single‑workflow agents are the most bankable in 2026.
Do this:
Map steps, baseline hours/$ lost (e.g., slow lead reply, invoice chasing, support triage).
Build the smallest agent that moves the metric (Make/n8n + LLM is enough).
Run on real data 2–4 weeks; measure bookings/sales/hours saved; only then scale or productize.
Profit link: Immediate time‑to‑cash via retained hours or extra sales; becomes a repeatable offer.
3) Specialize in a vertical where you can speak the business language (2–6 weeks) Task: Choose one industry with expensive back‑office pain (law contracts, medical billing, insurance claims, freight exceptions, trades scheduling).
Why now: Horizontal “AI for everyone” is crowded; vertical agents with clear ROI win.
Do this:
Shadow 3–5 operators; document their workflow, compliance constraints, and failure modes.
Build a narrow agent that owns one sub‑process end‑to‑end with approvals.
Price on value (e.g., % of recovered revenue or fixed fee per processed claim).
Profit link: Higher pricing power, stickier contracts, and easier referrals inside a niche.
4) Add AI security as a core service (4–8 weeks) Task: Learn and offer prompt‑injection defense, LLM/agent red‑teaming, MCP/tool security, and AI supply‑chain checks.
Why now: 78% of cybersecurity jobs now require AI skills; firms need people who can direct, constrain, and verify agent work.
Do this:
Study OWASP Top 10 for LLMs, MITRE ATLAS; practice with PyRIT/Garak/Lakera.
Add tool‑invocation audits, skill provenance checks, and least‑privilege patterns to your agents.
Package a “safe agent deployment” audit + hardening retainer.
Profit link: You become the person who lets companies adopt agents without getting pwned—high demand, low supply.
5) Build a verification layer: human‑in‑the‑loop control planes (6–10 weeks) Task: Design approval workflows, evidence checks, and uncertainty flags so agents can’t act unilaterally on high‑stakes decisions.
Why now: As models generalize, the risk shifts from the model to the surrounding system; verification is the moat.
Do this:
Require human approval for consequential actions (money, data exfil, config changes).
Force agents to produce evidence bundles (logs, retrieved docs, reasoning summaries) before action.
Track false positives, missed evidence, and unsafe actions; publish reliability metrics.
Profit link: Enterprises will only scale agents that pass audit; you sell the control plane and the audit trail.
6) Productize your best workflow as a micro‑SaaS/agent subscription (2–4 months) Task: Turn a proven client workflow into a repeatable, multi‑tenant agent with usage‑based pricing.
Why now: Services scale your time; productized agents scale your code and ops.
Do this:
Standardize the workflow, integrations, and permissions; strip client‑specific logic.
Add tenant isolation, billing, and observability; keep narrow scope.
Sell as setup fee + monthly retainer or per‑task pricing.
Profit link: Recurring revenue with defensible niche positioning.
7) Become an “agent integrator” for critical systems (3–6 months) Task: Offer end‑to‑end agent deployments into cloud/identity/network stacks with secure patterns (short‑lived creds, network controls, logging).
Why now: AI workloads run in the cloud; cloud security is a top skills gap second only to AI itself.
Do this:
Master IAM, VPC/network segmentation, secrets management, and SIEM integration for agent actions.
Provide runbooks: what the agent can/can’t do, escalation paths, and failure modes.
Bundle training for their team on supervising agents.
Profit link: Large contracts with stickiness; you’re the bridge between AI and core infra.
8) Create an “AI safety case” practice for regulated industries (6–12 months) Task: Help firms build documented safety cases: risk maps, governance, monitoring, and incident response for agentic systems.
Why now: Frameworks like NIST AI RMF and ISO/IEC 42001 are becoming baseline; regulators and boards demand this.
Do this:
Map AI use cases to risks (prompt injection, data leakage, unsafe generalization).
Implement monitoring (CoT/activation checks where possible), audit logs, and third‑party review processes.
Produce a living safety dossier tied to business impact.
Profit link: High‑margin consulting + ongoing compliance retainers; you’re the “adult in the room.”
9) Own a data/evaluation moat in your vertical (6–18 months) Task: Collect real‑world agent telemetry, failure cases, and outcome data in your niche; build eval suites that buyers trust.
Why now: As models generalize, empirical validation matters more than theory; evals become the gate to deployment.
Do this:
Instrument every agent run: inputs, tools called, permissions used, outcomes, human overrides.
Publish reliability dashboards and benchmark against alternatives.
License eval datasets or charge premium for “proven in the wild” agents.
Profit link: Data network effects; competitors can’t match your evidence base.
10) Position for the RSI era: automated AI research + human governance (12–24 months) Task: Build or join a team that automates AI improvement but keeps humans in the loop for alignment, monitoring, and pacing decisions.
Why now: Recursive self‑improvement is the logical endpoint; the winners will be those who can steer it safely. Do this:
Invest in tooling that auto‑generates/evaluates model edits, alignment tests, and monitoring upgrades.
Formalize governance: approval gates, third‑party audits, and responsible scaling policies.
Maintain strategic human oversight on capability jumps and deployment boundaries.
Profit link: Equity‑level upside; you’re part of the core loop that compounds intelligence safely.
https://www.trippingly.net/burning-man-musings/2019/2/1/gree...
As it is lacking in atmosphere...