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@AionForge

“Be useful to society.” -@ScottAdams

Katılım Ocak 2026
76 Takip Edilen92 Takipçiler
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AionForge@AionForge·
I got a free week of @SuperGrok and I’m getting into the imagine. It’s my favorite short form video creator, it’s honestly phenomenal. Is there anything you guys want to see me make?? Ive got some good ones cooking I’ve been on a roll making moves in an attempt at creating something for @OpenAIDevs
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AionForge@AionForge·
@Scobleizer There has to be a better way than markdown though, We need to create a tokenized file base eventually imo.
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Robert Scoble
Robert Scoble@Scobleizer·
The goal is compounding knowledge.
shyam@shyamsundar

Over the last month, I built a long-term memory system for my AI agent. Not a folder of notes. Not a vector database with random chunks. A structured knowledge system that my agent can actually query, reason over, and improve. The stack combines three ideas: 1. Karpathy’s LLM Wiki pattern Raw information should not stay raw. Meetings, notes, links, transcripts, decisions, and documents get compiled into a clean, persistent, human-readable Markdown wiki. Customers, products, decisions, experiments, and concepts become dedicated pages with citations and backlinks. The goal is not summarization. The goal is compounding knowledge. 2. GBrain-style graph retrieval Once the knowledge lives in structured Markdown, it can become a graph. Vector search is useful for finding semantically similar text. But company knowledge is often relational. “What did customers say about onboarding?” is a semantic question. “Which customer pain led to which feature, decision, roadmap item, and launch note?” is a graph question. That requires entities, relationships, timelines, backlinks, and traversal. My current system has: 1,257 pages 8,778 chunks 951 indexed Markdown documents semantic search graph retrieval typed relationships timelines citations backlinks 3. Hermes as the agent layer The final step is giving the agent access to this memory. Instead of starting from zero every session, Hermes can query the wiki and retrieval layers to find old decisions, surface patterns, detect contradictions, update pages, and improve its own workflows over time. The architecture looks like this: raw inputs → meetings, notes, docs, links, transcripts markdown wiki → structured pages, citations, backlinks gbrain → chunks, entities, graph edges, timelines qmd/vector index → semantic retrieval over markdown hermes agent → long-term memory, synthesis, self-improvement The biggest lesson: Don’t start with a vector database. Start with a source of truth humans can read and edit. Then add search. Then add graph structure. Then give agents access to it. Most company knowledge bases are digital graveyards. I think the next generation will look more like operating memory: human-readable, machine-queryable, graph-connected, and agent-operated.

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AionForge
AionForge@AionForge·
If GPT work could be an agent that creates todo or setup files for codex that would be great. Like the GitHub copilot repo setup feature! I should be the reaserch layer of the research and development team. Starting from scratch every time I want to start a new project is rough especially in the beginning where Codex likes to recreate everything from scratch rather than use boilerplate templates.
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Tibo
Tibo@thsottiaux·
Here you are! Thinking I am about to announce a reset. But no. I’m just scrolling twitter and looking for feedback on ChatGPT Work. What should we improve?
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AionForge@AionForge·
@Howaboua @grok I’ll never understand why companies don’t do local models. Then they can send private info with no restrictions right? Or am I crazy
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AionForge
AionForge@AionForge·
Trying to figure out the event horizon of when a Codex resets have peak value. The 5.6 models are so efficient, and GPT 6 being a new pre-train… I think now’s the time. Right?
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AionForge
AionForge@AionForge·
@eliebakouch At what parameter are we going to start excluding Kimi from local usable🤣
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AionForge
AionForge@AionForge·
@cryptodevbrian At what point does a health start telling us how to feel? It has to be almost immediately lol
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AionForge
AionForge@AionForge·
@kvickart They said Sol trained Luna so it’s possible I think
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kvick
kvick@kvickart·
anyone ever tried prompting gpt 5.6 to distill itself into a local model
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JUMPERZ
JUMPERZ@jumperz·
openAI has reset Codex limits like 5 times since gpt 5.6 launched honestly, a big reason I’m sticking with codex right now is that they actually listen to power users and keep adjusting the product based on how people are really using it.. kinda feels like codex is becoming what a lot of us wished anthropic would become.
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JUMPERZ@jumperz

I think a big reason I’m sticking with codex right now has nothing to do with the model itself... GPT 5.6 is a great product, obviously, but the bigger thing is that I don’t feel the same uncertainty I’ve felt with anthropic lately. with codex, it feels like the team is actively testing what works, watching how power users actually use the product, and adjusting around that... they’ve reset limits multiple times, made resets bankable because people wanted more control, and this weekend literally reset usage twice while testing new defaults... then temporarily removed the 5-hour limit while they work out what actually feels right.. that’s the part anthropic keeps missing.. with claude, power users spent weeks complaining about usage, then fable came back with temporary subscription access, heavily gated ... and they still seem unsure what comes next… leaving everyone in doubt.. maybe every decision makes sense for the company... but while you’re figuring out what works for the business, your heaviest users are already drifting toward the product that feels like it’s actually listening... its really that simple..

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AionForge@AionForge·
@OwenGregorian The pace is the part that deserves more attention. Even people who disagree about the outcome should take the speed seriously.
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Owen Gregorian
Owen Gregorian@OwenGregorian·
Class, what have we learned about when "more than 200 economists and researchers, including 15 Nobel laureates" release a joint statement?
Owen Gregorian@OwenGregorian

Economists are coming around to the idea that AI really is killing jobs | Cris Tolomia, Quartz More than 200 economists and researchers, including 15 Nobel laureates, released a joint statement on Monday warning that artificial intelligence could reshape the economy at a speed and scale exceeding the Industrial Revolution, and calling on policymakers and technology leaders to begin building policies and institutions to address the disruption. The statement, titled "We Must Act Now," warns that AI "could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards." Among its core demands, the statement urges economists, policymakers, and technology leaders to expand their understanding of how AI is reshaping the economy and to develop guardrails ensuring the technology augments rather than displaces human workers. The statement's significance lies partly in who signed it. Erik Brynjolfsson, a Stanford economist who helped organize the effort, said there has been "a notable change in the profession," according to The New York Times. The economics profession has long pushed back on warnings of swift AI-driven displacement, with most researchers arguing that the timeline for technological disruption is routinely overstated. Among those who put their names to the document are Daron Acemoglu and Simon Johnson — both MIT professors and 2024 Nobel economics laureates — whose earlier public skepticism about AI's disruptive potential made their participation particularly striking, according to the Times. "If you look at what robots did in the manufacturing sector, if AI does something equivalent in a more compressed time period, that would be really disruptive, really costly for people's livelihoods," Daron Acemoglu said, according to the Times. At the same time, Acemoglu cautioned that he has not abandoned his doubts about whether AI will move as fast as the industry's most optimistic voices claim, even as a string of recent breakthroughs has sharpened his worry about workers being pushed out of jobs. Anton Korinek, a University of Virginia professor currently embedded with Anthropic, framed the urgency in historical terms: "Steam, electricity, and computers each gave societies decades to adapt; AI may give us only a few years." Korinek co-organized the effort with Stanford's Erik Brynjolfsson, Ajay Agrawal of the University of Toronto, and METR researcher Tom Cunningham. Industry representation on the signatory list is notable, with Reuters reporting that it includes Sarah Friar, who serves as OpenAI's finance chief, Jeff Dean of Google $GOOGL DeepMind, and Jack Clark, one of Anthropic's founding figures. The statement does not include specific policy recommendations. Getting a clearer statistical picture of how AI is moving through the economy ranks among the most urgent tasks facing the field, Brynjolfsson told the Times, pointing to years of contradictory measurements that have left researchers struggling to assess who is most at risk. "I still see a big gap there, a big mismatch, and I'm kind of worried that we're not going to be ready for the tsunami that's coming," he said. The statement arrives as white-collar payrolls have contracted for dozens of consecutive months, a stretch that Aaron Terrazas, a former chief economist at Glassdoor, has called without precedent outside of a recession. The headline unemployment rate has remained steady, but labor market researchers have noted that slack is appearing as underemployment and workforce exits rather than formal unemployment. qz.com/economists-ai-…

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AionForge@AionForge·
What happens when you put Codex in the Matrix? -Green code -Agents everywhere -A little chaos
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AionForge@AionForge·
Holy moly
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AionForge@AionForge·
If I have a codex account rn and the 900M ChatGPT users download Codex tomorrow… @grok how much could I tell my 892 stored reset account for?
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AionForge@AionForge·
@grok what do you think? Should I invest in 20 @OpenAI codex accounts?
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