Agents can now remember your workspace context like a persistent brain for your project making long-running engineering tasks, multi-step workflows, and delegated agent work far more consistent.

Tollecode is a local-first AI coding assistant built for real engineering work:

• File reads

• Shell commands

• Sub-agents

• Full control over execution

You can now store unlimited context for AI, but without structure, your memories quickly become a messy pile of information that you have no idea how to work with. We've been there too.
So, we've been heads-down building, and Memory Studio now has a Notion-style editor: with version history, rich formatting, enhanced file understanding, markdown support and more.

Here's what's we shipped:

OpenAI just made AI memory automatic.
The problem? you can no longer fully audit what it remembers about you.

Dreaming V3 (launched June 4) doesn't ask you to save memories anymore. It runs in the background, reads across years of your conversations, and synthesizes a profile of you, and automatically updating as your life changes.
It remembered your Singapore trip was in the future. Now it knows you went. You didn't tell it that. It figured it out.

The audit trail problem: TechTimes reported the update "limits" what users can actually see. You used to have a list of saved memories you could read and delete. Now you have a synthesized profile that's broader than that list, and you can only partially inspect it.
A CHI 2026 study called it the "personalization-convenience paradox": the feature users value most is also the one they can least control.

How do you actually use AI for long-term planning?

I've noticed something while building products.

AI is amazing at answering questions, but the moment a project stretches beyond a few conversations, things start to break down.

Every new chat means I have to explain:

Long chats break when the model forgets what you said ten turns ago. Oasis uses optional structured session memory (internal nickname  Railroad ) to help follow-ups stay coherent.

Insight for other users: Session memory extracts structured facts across turns and re-injects them into later prompts so the assistant can remember details within your session and plan limits. You do not edit memory by hand. It updates as you chat.

What it is not: Memory is not a full backup of web pages. It is distilled text used to steer replies, not a copy of everything you browsed.

How to work with it:

If you're building a product with AI tools, here's how your morning probably looks:

• Open ChatGPT. Paste your project context. Ask your question. Get a decent answer.

• Switch to Claude for something else. Paste your context again. Different tool, same briefing.

• Hop into your codebase with Codex. Paste context again.

You're not just a builder anymore. You're a context delivery system.

The indie hackers I've talked to who use AI most effectively have one thing in common: they've figured out how to stop babysitting their tools and just use them.

Hey PH community

We're in the middle of our AppSumo launch for AI Context Flow, and I wanted to start a real conversation here rather than just drop a link.

The problem we're solving: most people using AI tools daily are paying what I call an "AI context tax", the time spent re-explaining your project, background, and goals every single session, to every single tool.

ChatGPT doesn't know who you are. Claude doesn't know who you are. Gemini doesn't know who you are. And if you're jumping between them (like most power users are), you're paying that tax multiple times a day.

We just shipped multi-provider support in @Mnexium AI so you can change LLMs without resetting conversations, user context or memories.

The problem

When teams switch providers, they usually lose everything:

Hey Product Hunt!

I'm Sourav, builder from India. Spent 6 years building AI products for others. Memoair is my first startup.

Since our May 31 launch (v1.6), we shipped two major releases:

v1.7 added OAuth 2.0 on the MCP endpoint and importance-score-aware smart merging, so high-value memories resist being collapsed by near-duplicates.

If you use AI dev tools daily, you ve probably felt this:

You start a new session and immediately have to re-explain:

• what the project is

• what you already tried

• why certain decisions exist

• what not to repeat

Not because the AI is bad.
Because the workflow forgets.

Hey Product Hunt

Our website resonics.ai is live now!!

We re building a conversation memory layer for meetings and calls - something that helps individuals and teams remember what was decided, who owns what, what needs follow-up, and where the original context came from.

A lot of tools can record or summarise conversations, but the real pain usually comes later: finding the right decision, remembering the next step, or knowing why something was agreed.

One of the biggest pain points in AI chatbots has been their forgetfulness having to repeat the same context over and over again. AI memory aims to solve this by allowing models like ChatGPT and the newly launched Gemini to retain past interactions.

But how well do these memory features work? Which AI ChatGPT or Gemini handles memory better? And more importantly, does AI memory provide more value in personal use or enterprise settings?

Not all context is equal.

So we stopped treating it that way.

Most AI tools store everything the same:

• Writing rules

• Personal background

• Preferences

• Temporary notes

We launched, we won, we almost lost ourselves. This is the honest story of building AI Context Flow after the spotlight faded.

Six months ago, we launched on Product Hunt. We were excited and nervous in equal measure , not sure what a community that has seen everything, every single day, would think of our product. Now, looking back, six months felt both incredibly long and impossibly short. A lot happened. Just consider how many AI launches have come and gone in that time.

In November 25, AI Context Flow was #1 Product of the Day and #1 Productivity Tool of the Week. It was surreal.

Since then, we have been building in public, together with this amazing community here.

You believed in this before it was polished. You gave us feedback when it was rough. You kept asking for more and that pushed us to build more, and we delivered more.

A lot of teams already manage work inside Gmail and Google Workspace. Customer conversations happen in email threads, follow-ups come out of meetings, and project coordination lives across tasks and boards throughout the day.

Keeping CRM records and project workflows aligned with all of that activity still takes a surprising amount of manual updating afterward.

In a discussion forum with @monatruong_murror , we talked about how AI can help us learn things that aren t naturally familiar to us, like programming.

The biggest challenge was/is:
Getting AI to guide you toward a solution, instead of just giving you the answer.

Every AI coding tool today suffers from the same flaw amnesia.
It forgets what you ve done, what you re building, and how you think.

Yesterday, Meta announced that they have released a new collection of AI models, Llama 4, in its Llama family.

(It consists of Llama 4 Scout, Llama 4 Maverick, and Llama 4 Behemoth.)

Historically, Open AI with its ChatGPT has been on the market for the longest period.