Method · SocialCoach Kyle demonstrates a complete approach sequence: open with a basic compliment ('I saw you walking by, I thought you looked very cute and fashionable'), ask 'how's your day going?', playfully misinterpret her answer (e.g., 'You dog walker?'), inject laughter, then make an observation/assumption about her (e.g., 'you probably do something artsy') and share personal input about his own experience.
Method, not a verified protocol.
Coach Kyle · Don't know what to talk about with women? Watch This
Method · MoneyTo kickstart your process internally within your organization, you can either talk to the speaker or his team, or you have six questions to start asking. These are like guideline questions for you to start asking to refine your tokenomics. The questions: (1) Who, team, spend what? (2) What must you test to use a cheaper model? (3) What's your base load? (4) What's the default model for each team? (5) Which workload has a person waiting that you need something faster? (6) How much of what we generate is useful?
Method, not a verified protocol.
AI Engineer · The AI Bill Is Too Damn High: Tokenomics 101 — Eugene Cheah, Featherless AI
Recommendation · MoneyBootstrap a fine-tuned model by starting with an expensive model that can already do the task: deploy it at small scale to generate your training dataset, then distill its outputs down to a 7B-scale model and fine-tune the collected data at different model sizes to see what fits best. Fine-tune models to specialize use cases once you reach scale.
AI Engineer · The AI Bill Is Too Damn High: Tokenomics 101 — Eugene Cheah, Featherless AI
Recommendation · MoneyNacho Martinez proposes creating vector embeddings out of the tools and skills; when the agent loop needs to retrieve useful skills or tools, do a vector similarity search against the vector store, pull the most relevant skill or tool, solve the problem, then offload it again — one of the ways to keep the context window relevant.
AI Engineer · Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
Recommendation · MoneyNacho Martinez describes context summarization as tackling context bloat: you ask the model to generate a short summary of everything said in the context window, then start the next conversation from that summary.
Contested by Jake on Figma
AI Engineer · Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
Recommendation · MoneyNacho Martinez recommends skill distillation — taking previous conversations and runs with an AI agent and creating something meaningful out of them, such as a skill MD file placed into the skill box — describing it as a technique to improve a frozen model's learning over time by controlling the data layer rather than the weights.
AI Engineer · Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
Recommendation · MoneyNacho Martinez proposes creating vector embeddings of tools and skills, using vector similarity search against the vector store to pull the most relevant skill or tool when the agent loop needs one, and offloading it again after solving to keep the context window relevant.
AI Engineer · Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
Recommendation · MoneyNacho Martinez recommends context engineering techniques that automatically summarize, compact, and shrink the working context window: asking the model to generate a short summary to start the next conversation from, and compaction — storing conversation chunks in a database or file with an ID that the agent can pull from with an offset when needed.
AI Engineer · Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
Recommendation · MoneyNacho Martinez describes compaction as tackling context bloat: you put chunks of the conversation into a database or file with an ID, and the agent later pulls from that ID with an offset, like reading from a file.
AI Engineer · Memory-Aware Agent Harnesses for Anti-Money Laundering — Nacho Martinez, Oracle
RecommendationSplit generation and evaluation into separate agents to get better results, using the advisor pattern with a generator agent and a judge agent that exploits the generator-discriminator gap. For really massive problems, a generator/judge roster won't scale without separate and distinct checkpoints, so spread work across many agents with adversarial review that has its own isolated context uninfluenced by previous checks.
endorsed
AI Engineer · Why One AI Agent Isn’t Enough (And How to Run 1,000) - CJ Avilla & Harrison Stall, Anthropic
RecommendationUse flows to split work so each service gets a dedicated agent context responsible only for its small scope, then fan out across all 50 services with an agent per service to check health, guaranteeing every service was checked.
endorsed
AI Engineer · Why One AI Agent Isn’t Enough (And How to Run 1,000) - CJ Avilla & Harrison Stall, Anthropic
RecommendationThe speakers recommend using explicit environment policy configurations to restrict agent access to specific domains or specific tools (e.g., web search but not web fetch), and disabling WebFetch and WebSearch by default so the agent has no internet access without explicit allowance. If the agent tries a non-whitelisted domain, the request physically fails at the firewall layer on egress.
AI Engineer · Why One AI Agent Isn’t Enough (And How to Run 1,000) - CJ Avilla & Harrison Stall, Anthropic
RecommendationJed recommends setting a budget on agent flows (e.g., $1.25) so that if the flow hits the budget and isn't finished, you can decide whether to raise the budget and let it continue without redoing work; budgets prevent agents from continuing to spend tokens unexpectedly. The model does not know its budget while running — the budget is a guardrail against runaway spend — because telling Claude its budget can make it 'anxious'. Work already accomplished is not thrown away if you up the budget.
AI Engineer · Why One AI Agent Isn’t Enough (And How to Run 1,000) - CJ Avilla & Harrison Stall, Anthropic
RecommendationCJ urges taking credential management seriously: use credential vaults to store secrets as environment variables rather than plain-text files, use totally fake secrets in every container with real credentials substituted only at the egress proxy layer, and configure the injection location and allowed host fields so a credential like a Twilio API key is only substituted on the correct domain. Pass the vault when creating the session so the platform injects correct values.
AI Engineer · Why One AI Agent Isn’t Enough (And How to Run 1,000) - CJ Avilla & Harrison Stall, Anthropic
Recommendation · MoneyAssign someone on your marketing team to monitor weekly which vendor AI agents recommend in your space, and work to stay number one. AI agent recommendations change over time, so vendor rankings in agent answers must be tracked continuously, not checked once. Don't rely on B2B apps that charge for weekly ChatGPT-ranking updates — they won't tell you what agents will pick. Ensure your product gets recommended by as many AI agents as possible, not just by chatbots like ChatGPT — agents running in tools like Replit, Lovable, and Claude Code increasingly make vendor recommendations for end users. When a human asks ChatGPT or Claude which CRM to buy, you want your product named in the top three of the answer, because that generates massive leads.
None; direct advice.
The Official SaaStr Podcast: SaaS | Founders | Investors · SaaStr 882: Claudeforce vs. Replit vs. Lovable. Who wins? (The Agents #016)
Recommendation · MoneyThe founders describe running a private Facebook group of 15,000 dads, being loyal back to the community by engaging daily, jumping into members' lives to help them through things, and staying in the group talking to people and sharing their stories themselves even 4.5 years later rather than delegating to a community manager.
described
Foundr · We Started Dad Gang with $750 and One Hat - and did $40M in 4 Years
MethodLeash's fix for nickname memorization is a loop: read cross-attention maps from the structure-binding model to locate where the model thinks the binding pocket is; find cases where the model is memorizing nicknames; then enrich training with large-scale single-residue mutations — proteins that differ by one tiny residue but behave very differently force the model to attend to all amino acids, including the pocket. After retraining with additional mutations, Hermes attends much more evenly. The underlying principle: vary the proteins as well as the small molecules when generating training data, because models that memorize pockets can be taught real physics only if binding regions are experimentally mutated and re-screened.
described as Leash's implemented loop
Method, not a verified protocol.
Latent.Space · Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio
Method · MoneyHari offers a three-question playbook for leaders who don't build: ask 'who has already done this?' — this question forces the search and tames the build-first reflex powered by AI; ask 'what do we have that the next team, our competitors, or another person in the team don't have?' — this forces novelty as a first-class citizen into the team's minds; and ask 'where will this application live?' — forcing outcome-based thinking when everyone is high on building shows the demo is the expensive way to find out. Run the four moves as your roadmap for next quarter's features — they are not side project rules.
endorsed
Method, not a verified protocol.
AI Engineer · I Spent Two Years Building the Median — Hariprasad Sudharshan
Recommendation · MoneyHarish Gaggar recommends building a centralized authentication and routing hub (MCP gateway) that securely connects any coding assistant tool or agents to endpoints via the MCP gateway and MCP server, and avoiding direct access to any endpoints: all access must go through the MCP gateway with proper authentication before reaching the MCP server and endpoints. All agent calls should flow: agent → MCP gateway for authentication → the right MCP server → API call to the endpoint → response returned.
AI Engineer · Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma
Recommendation · MoneyHarish Gaggar recommends ensuring MCP servers have the right tools to execute queries, building customized MCP servers with the right tools in place, and leveraging the FastMCP server package or wrapper to build them with different sets of tools inside the wrapper. Every tool defined in an MCP server must be fully reviewed by the internal security team so only authorized tools go to production, and filters, limits, and block fields should be enforced in MCP tools so fields not required by the LLM are blocked. In air-gapped environments, he says it is highly recommended to build your own MCP servers rather than using externally available ones.
AI Engineer · Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma
