oasisers99 Most content teams have a blog archive full of posts that did real work when they were published and have been slowly gathering dust since. Some are outdated. Some are thin. Some are good but simply haven’t been promoted since launch. All of them represent time and expertise that could be doing more work with … Read more
Castmagic and Descript are both AI tools that start with audio or video and produce useful content from it. But comparing them as if they’re the same kind of tool creates confusion — they solve meaningfully different problems and excel in different parts of the content workflow. Understanding what each one is actually designed for … Read more
An interview with a good subject is one of the most content-dense raw materials available. In forty-five minutes of conversation, a knowledgeable person will share insights, frameworks, experiences, and opinions that would take months to accumulate independently. The problem is that most interviews become one article, maybe a few social posts, and then the transcript … Read more
Every time you open a new AI chat and type “I’m a marketing manager at a B2B SaaS company and I need help with…” you’re spending time on context that should already be there. The AI that was genuinely useful yesterday has no memory of it today. That friction is small per conversation and significant … Read more
As AI agents move from demos to production workflows, the question of how they manage memory across sessions becomes a genuine architectural decision. Zep and Letta both address this problem, but with fundamentally different philosophies: Zep adds memory to agents built on existing frameworks, while Letta is itself an agent framework where memory is a … Read more
Most AI agents run the same task the same way every time. They have instructions, they have context, and they execute. What they don’t have is any memory of what happened last time they did something similar — whether an approach worked, what went wrong, what a particular supplier tends to do, or how a … Read more
Every message in a conversation costs tokens. In short interactions this is irrelevant — even a twenty-turn conversation fits easily in a standard context window at negligible cost. But AI assistants built for extended interactions — customer service agents handling long support threads, project management assistants tracking months of decisions, research assistants building on weeks … Read more
AI models are stateless by default — each new conversation starts from scratch, knowing nothing about the last one. For a one-off task this is fine. For a business AI assistant that interacts with the same people repeatedly, on the same projects, with the same preferences, it’s a significant limitation. The memory layer is the … Read more
Context window size has become one of the most-discussed AI model specifications — with some models now offering windows of a million tokens or more. But bigger doesn’t automatically mean better for most business use cases, and understanding when context window size actually changes outcomes (versus when it’s just a spec number) helps you make … Read more
Memory in AI tools has gone from a novelty to a genuine architectural decision. If you’re building or choosing an AI assistant for business use, the question isn’t just “does it remember things?” but “how does the memory work, what does it remember, and can it scale to what we actually need?” Mem0 and Claude’s … Read more