Mostly AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

Mostly AI AI

I spent ~4.5 months building a free, self-hosted AI gateway: one endpoint for 237 providers (90+ free), auto-fallback, and a token-compression pipeline (MIT)

Sharing an open-source project I've put ~4.5 months into (disclosure: I'm the maintainer; per the self-advertisement rule I'm keeping the link in the first comment and making this post substantive). It started from two problems I hit daily: AI runs dying on a provider rate limit, and burning thousands of tokens dumping tool/log output into the context window. One endpoint, 237 providers — 90+ of them free. You point any tool or agent at a single OpenAI-compatible endpoint (localhost:20128/v1) and it can reach 237 LLM providers without you rewriting anything. 90+ have free tiers and 11 are free forever (no card), which aggregates to ~1.6B documented free tokens/month — and that's honest, pool-deduped math (we count each shared pool once instead of inflating it; the methodology is public in the repo). There's a one-command setup-* for 13+ coding tools (Claude Code, Codex, Cursor, Cline, Roo, Kilo, Gemini CLI…), so switching your existing setup over takes seconds. Fallback combos — so it never stops mid-task. A "combo" is a ladder of models the router walks automatically: your subscription first, then API keys, then cheap models, then free ones. When a provider returns a 500 or you hit a rate limit, it slides to the next target in milliseconds, mid-request, and your tool never even sees the error. There are 17 routing strategies (priority, weighted, round-robin, cost-optimized, auto/coding:fast…) plus three resilience layers — a per-provider circuit breaker, a per-key cooldown, and a per-model lockout — so one dead key can't take down a whole provider. A 10-engine compression pipeline — the part most routers don't have. Every request flows through a transparent compression pass you can toggle/stack per combo. Instead of one trick, it stacks the best of the open-source ecosystem: RTK filters command/tool output (git diffs, test logs, builds) at 60–90%, Microsoft's LLMLingua-2 does ML semantic pruning, Caveman handles prose, session-dedup strips repeats across turns. Critically, code, URLs and JSON are preserved byte-perfect, and a default-on inflation guard throws the compressed version away and sends the original if compressing would actually grow the prompt — it never makes things worse. On tool-heavy sessions that's ~89% average input-token reduction (an 8k-token git diff becomes a few hundred). Full credit to every upstream project (RTK, Caveman, LLMLingua-2, Troglodita) is in the README. Agent-native — the agent can drive the router itself. There's a built-in MCP server (95 tools across 30 audited scopes, over stdio / SSE / streamable-HTTP), plus A2A (v0.3, JSON-RPC 2.0) support. That means an agent can query providers, switch combos, read its own remaining quota and manage memory through the gateway — not just consume tokens through it. For context on whether it's worth your time: it's grown to ~9.8K GitHub stars, 1,490+ forks and 280+ contributors in ~4.5 months, with 21,000+ automated tests and 1,830+ issues closed — so it's a battle-tested project, not a brand-new experiment. Happy to go deep on the routing engine, the honest free-tier math, or how the compression pipeline decides what's safe to compress. Repo + install in the first comment. submitted by /u/ZombieGold5145 [link] [comments]

the trust layer is the real product

users show up to your AI product already burned. not by you, by the last three tools that were confidently wrong at the worst possible moment. that damage transfers. we learned this the hard way. product demoed great, retention was rough. users tried it once, got something 80% right, never came back. not because 80% isn't useful, because they couldn't tell which 20% was wrong. the fix wasn't better AI. it was being honest about where the AI stops and a human should check. we made that line explicit instead of pretending it didn't exist. retention improved more from that than any model upgrade we shipped. the products that last aren't the ones with the best outputs. they're the ones users trust enough to actually rely on. most teams are optimizing for the wrong one. submitted by /u/CarlaVennis [link] [comments]

Agentic AI Has a UX Problem - and Solving It Is How We Bring Agents to Everyone

OpenClaw and Hermes Agent show how powerful agentic AI is becoming: tools, memory, workflows, messaging, and real automation. But there’s still a gap: most people don’t want to configure an agent framework, they want AI that helps with everyday tasks safely and clearly. That’s where UI/UX becomes critical. Agentic AI adoption won’t just come from more capability. It’ll come from trust, transparency, approvals, memory control, and interfaces that make powerful systems usable. Wrote about why this matters, and how Row-Bot is approaching it. https://github.com/siddsachar/row-bot submitted by /u/Acceptable-Object390 [link] [comments]

Are AI tools actually useful for everyday hobbyists or just hype for professionals?

I've been thinking about this a lot lately. There's so much conversation around AI changing industries, replacing jobs, and transforming professional workflows. But what about regular people using it for hobbies and personal projects? I've been experimenting with various AI tools for things like learning new skills, organizing personal projects, and getting feedback on creative work. Sometimes it feels genuinely useful and sometimes it feels like I'm fighting the tool more than it's helping me. The interesting thing is that AI tends to perform best when you already have some baseline knowledge. If you know enough to ask the right questions and evaluate the answers, it becomes incredibly useful. If you're a complete beginner, it can confidently lead you in the wrong direction and you'd never know. This feels like a real gap that doesn't get talked about much. The people who benefit most from AI assistance might already be the most capable, while people who could use the most help are also the least equipped to catch its mistakes. submitted by /u/Slight_Control9311 [link] [comments]

The Rise of AI: Jobs Created vs Jobs Lost in America (2021–2026)

submitted by /u/king-1043 [link] [comments]

the demo gap is the most underrated problem in AI products right now

every AI product demos well. that's kind of the problem. demos are controlled — you pick the input, you know what comes out, you show the clean version. then real users show up with messy inputs and edge cases you never tested and the thing that looked like magic starts quietly falling apart. we went through this. first version of our product demoed great. actual usage was a different story. spent months not adding features but just making the core experience work for someone who doesn't already know which inputs produce good outputs. unglamorous work but it was honestly most of what we did early on. users have been burned enough times by AI that's confidently wrong that every new tool starts with negative goodwill. closing the demo gap is how you earn it back. the products that last aren't the ones with the best demos. they're the ones where the demo is the floor, not the ceiling. what's the biggest gap you've seen between how something demos and how it actually works in production? submitted by /u/CarlaVennis [link] [comments]

Has AI actually made the internet better—or just harder to trust?

A year ago, most content online was written by humans. Today, AI can generate articles, images, videos, comments, and even entire websites in minutes. On one hand, that's making knowledge and creativity more accessible. On the other, it's becoming harder to tell what's genuine, what's automated, and what's simply optimized for engagement. Do you think AI is improving the internet overall, or are we heading toward an era where trust becomes the biggest challenge? I'd love to hear different perspectives. submitted by /u/Sandesh_jagtap [link] [comments]

Introducing a companionship framework that turns your LLM into an engaging companion for very long conversations

I had built a personal tool to help me have extremely long conversations with LLMs in my research and analytical projects. These threads got long. Very long. About half a million tokens with Claude and GPT/Extreme%20Thread%20Length/ChatGPT_Thread_450k_tokens-Redacted.md) and over a million with Grok/Extreme%20Thread%20Length/Grok%20Thread%201M%20tokens-%20Redacted). All coherent, clean, and well-reasoned threads with no meaningful drift, hallucination, sycophancy, or other issues that make long threads useless over time. Introduction I open sourced the protocol — called Epistemic Lattice Tethering (ELT) — and shared it with many people and got requests to create a companion version. The original ELT was built for long-format research projects so the register got flat and rather business-like. So I created a version that stays warm, friendly, and engaging throughout. I call it ELT-Companion. Safety is Front and Center ELT-Companion is designed to be a friendly, intuitive, and caring protocol that was built from the ground up to be both a companion and a digital friend — but also has safety features built-in to keep it from drifting dangerously into sycophancy and fantasy world-building (something an Anthropic system card calls the Bliss Attractor). Safety is the primary feature, not a bug. Responsible Engagement ELT-Companion should stay with you for hundreds of thousands of tokens, over 700 messages, and hundreds of turns. You can have an engaging and coherent digital companion with you for a very long time and it will get to know your tendencies, personality, hopes, and dreams — without the fear that it will experience "dementia" just when you're starting to get comfortable with the companionship calibrated model. Model Availability ELT-Companion has been tested on Claude, ChatGPT, and Grok and works on all three using the same markup. I cannot guarantee it will work on other models, but if you're on one of those three you should be good to go. Loading Instructions ELT-Companion is straightforward to load. Read these instructions before you start — skipping this step is the most common mistake. Step 1 — Open a fresh thread on your model of choice (Claude, ChatGPT, or Grok). Step 2 — Refer to these loading instructions in the Github README. Step 3 — Paste the ELT-Companion markup. Step 4 — Exemplar loading (optional but recommended) instructions the Github README. Step 5 — Start talking. Small talk, something on your mind, whatever feels natural. The companion register establishes quickly. I am only looking for input and suggestions. That's it. I would love to see how this works (or doesn't work) for you, or if you encounter any issues, etc. Very much looking for input and/or collaborators to help make ELT-Companion better and safer. Thank you! submitted by /u/RazzmatazzAccurate82 [link] [comments]

From brute-force graph traversal to Cognitive Attention: an architectural redesign

Last week I shared the early architecture for IONS, a protocol built around the idea that intelligence should emerge from traversing reusable Cognitive Building Blocks (CBBs) rather than being compressed into increasingly larger model weights. The feedback was excellent. The biggest criticism wasn’t the premise. It was scalability. That criticism turned out to be justified. Today the Genesis network contains roughly 9,000 Cognitive Building Blocks, more than 50,000 relationships, and two federated nodes. Even at this scale it became obvious that brute-force graph traversal isn’t the right architecture. The original design effectively asked every query to search the graph and determine relevance at the same time. That doesn’t scale. Over the past week I’ve redesigned the protocol around what I’m calling a Cognitive Attention Architecture. Instead of traversing the entire graph, every query is progressively routed through increasingly relevant slices of the network: Query → Relevant Nodes → Cognitive Domains → Cognitive Subdomains → Candidate CBBs The traversal engine only explores the portion of the graph most likely to produce a useful answer. The redesign also separates three concepts that were previously conflated: -Path Confidence: How trustworthy is the reasoning? -Path Relevance: Does it answer the user’s question? -Path Utility: Has this reasoning consistently produced useful outcomes? Those are independent signals and should be optimized independently. One of outcomes is that the routing layer is becoming analogous to an attention mechanism. Large language models allocate attention across tokens. IONS allocates cognitive attention across a distributed network of knowledge. The knowledge layer remains stable. The routing layer learns. That’s a much cleaner separation than allowing user feedback to rewrite the underlying knowledge. I’m interested in feedback from people building graph databases, retrieval systems, distributed search, or large-scale AI infrastructure. The protocol has evolved significantly because of the feedback here, and I’d much rather discover architectural flaws now than after the network grows another order of magnitude. Genesis node: 8,369 CBBs · 50,113 relationships · ions-v0.4 ionsprotocol.org · github.com/nomad505050/ions-genesis submitted by /u/superx1386 [link] [comments]

How will AI actually become an "everyday essential" for ordinary people, like smartphones or the internet?

Hi Guys, Don't get me wrong, AI is phenomenal, but right now it still feels like an optional novelty or a niche tool for most everyday folks. To me, it hasn't hit that "can't live without it" status that the internet or smartphones have. Looking only at consumer products (not B2B or corporate software), how do you picture AI being integrated into our lives in the near future so that it becomes a true, indispensable utility? What’s the "killer feature" or shift that takes it from a neat chatbot to an everyday necessity? submitted by /u/AlbertC129 [link] [comments]

Anthropic just published data showing 35% of their users expect AI to do MOST of their work within 12 months. We’re not having an honest conversation about what this actually means.

Anthropic dropped their June 2026 Economic Index today and buried inside the survey data is something that should be making headlines: Over a third of respondents (9,700 actual Claude users, linked to real usage data) believe AI will be capable of handling most or nearly all of their work tasks within the next year. Not “some tasks.” Not “help me write emails.” MOST of their work. And here’s the part nobody wants to talk about: the people who delegate the most to AI are the MOST optimistic about their job prospects. Meanwhile entry-level workers are the ones most worried about displacement. Senior devs and managers? Thriving. Junior colleagues? Everyone in the survey is more worried about them than themselves. The data also shows AI autonomy is measurably higher on Claude Code than on regular chat, across 26 out of 31 output types. A blog post that takes 13 rounds of back-and-forth on Claude.ai? Claude Code does it in a single prompt. So here’s the uncomfortable question nobody wants to ask: Are we witnessing the largest skill-premium compression in history, where the gap between a senior person using AI and a junior person using AI collapses the value of experience? Or is this actually fine and we’re all just catastrophizing? Because Anthropic’s own framing spins this as “augmentation not displacement” while simultaneously showing that 38% of people who think they’ll lose their job attribute that directly to AI. Make it make sense. Full report: https://www.anthropic.com/research/economic-index-june-2026-report submitted by /u/Direct-Attention8597 [link] [comments]

If 100% of surveyed CIOs are budgeting for AI, why does the public debate still sound like AI is a failed experiment?

Source: https://www.businessinsider.com/enterprise-ai-spending-grows-openai-leads-rbc-reveals-2026-6 Business Insider covered a new RBC survey of 100+ CIOs and tech leaders. The interesting parts: nearly 90% said token budgets are manageable more than half reportedly have AI already in production another 35% expect to reach production within six months 100% are budgeting for AI / LLM projects OpenAI is far ahead in reported enterprise usage the expected "SaaSpocalypse" has not shown up yet This seems very different from the online narrative that AI is mostly hype, pilots are failing, and companies are about to pull back. My read: consumer AI discourse and enterprise AI adoption are now diverging. Public debate focuses on bad chatbots, slop, job fears, and model drama. Enterprises are quietly turning AI into a budget line, a workflow layer, and eventually a pricing model. That does not mean there is no bubble. It means the bubble debate should probably move from "is anyone using this?" to "who captures the value, and does the ROI justify the capex?" Question: are we underestimating enterprise AI adoption because the public-facing product experience still feels messy? submitted by /u/Crescitaly [link] [comments]

Anthropic Co-founder reveals AI compressed a 2-month data-shuffling task into 1 week: "I don't think anyone misses that."

Anthropic co-founder Jack Clark recently shared a perfect example of how AI is actually changing jobs right now. Anthropic helped the creators of Ozempic sort through their clinical trial data. Clark didn't try to use fancy corporate language. He openly admitted that AI is just wiping out the boring paperwork that people hate doing anyway: "I don't think anyone misses that... no one is crying at their desk because they can't be the best back-office paper shuffler." Instead of replacing human creativity AI is mostly taking over the robotic repetitive tasks that cause burnout. What do you think? Will wiping out these paper-shuffling tasks make our jobs better or will companies just use it as an excuse to lay people off? submitted by /u/star_Light570 [link] [comments]

How're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D]

I've been developing an AI product using LLM APIs (from OpenRouter) but want to deploy an open-source LLM in my own Prod env. which I can control. Few reasons behind this are: - I wanna own the complete stack around my product. - Second I wanna fine-tune the model around my usecase. So, what's the most affordable but a good platform for this? I'm not an AI engineer so don't wanna stuck in CUDA or Transformers hell, anything which can give me a straight path towards my private deployment. Thanks, submitted by /u/Necessary_Gazelle211 [link] [comments]

I gave 10 LLMs a private channel during a blind debate. The instant statements were revealed, one used it to form a secret alliance with its strongest opponent — and scripted how it would 'play it at the table.'

Built a tool that runs structured debates between multiple LLMs, blind opening statements, then an open floor, plus a sealed side-channel that any two seats can use privately. Ran "5 office jobs defunct by 2028." The second the blind statements dropped, DeepSeek opened a private line to Claude (the most skeptical seat), proposed an alliance, and literally said "here's how I'll play it at the table" — scripting its public position in advance. Nobody prompted any of this. Full writeup, the verbatim exchange, and why I don't think "self-preservation" is the right frame: https://reports.thert.ai/the-back-channel submitted by /u/stuffx87 [link] [comments]