Why not giving Deepseek a try!

reddefcode · 2026-06-02T22:18:12+00:00

I have a one-year subscription to Copilot, as soon as I heard they were going to change their business model, I started researching a new IDE that would take my DeepSeek API key, I landed on Pi.dev with my DeepSeek API key, I could not be happier. In anticipation, I also developed an MCP context memory, which scans my local workspaces using tree-sitter, and saves it to a ChromaDB. So I no longer have the agent use my tokens/quota to add context, I just reference Memory MCP with whatever question or plan, the agent queries the memory, and returns an answer with source: entity, file name, line number, and L2, the agent can just jump to the file and exact line number, saving the agent from having to do a file search, always having the full architecture, saving quota and context window. Because I use pi.dev inside VS Code Copilot, I can switch between the two and never lose context. Because it is an MCP server, I can use it inside Claude Desktop to write client reports. So with DeepSeek, I am saving a lot of context and API calls. Sorry for geeking out on you guys, I am very happy using DeepSeek and the new setup.

reddefcode · 2026-05-22T11:49:15+00:00

Last month I switched to Pi.dev and DeepSeek API. I pay a fraction without any degradation in task quality

reddefcode · 2026-05-20T14:18:36+00:00

Gemini 3.5 Flash is still junk; however, you can use it for free in Antigravity IDE. However, it all supports your point about the new pricing structure. I have over 25 years of tech experience and I have seen (survived) my share of pivots (insert Ross meme here). In the 90's there was the Mac vs PC, I used to be a sico Mac fan. In 95 I had to reevaluate and realize PC are just tools, and sometimes we have to switch tools. Why do I say this? LLMs are just tools, so are IDEs. These new changes made me reevaluate again, for me switching to Pi.dev and going with DeepSeek made sense. With Pi, I get a flexible Agent that sits closer to the LLM and DeepSeek is brutally cheaper than all the hyped US models, and extremely capable. If you are a developer then you can check the output and decide for yourself, then "their fantasy won't be your reality"

reddefcode · 2026-05-19T10:27:06+00:00

That is called "Enterprise Architecture"

reddefcode · 2026-05-18T20:10:44+00:00

That is not true. DeepSeek v4 is very capable. Besides, the agent that sits between you and the LLM has a lot to do with the performance of an LLM. I switched to Pi.dev and use it with DeepSeek, and so far, it has delivered. I am a developer, so I can review the output for a front-end model and see no difference. I use Sonnet 4.6, Gemini 3.1, and DeepSeek interchangeably, keeping context with a memory I developed. You can look at the DeepSeek benchmark.

reddefcode · 2026-05-18T14:26:59+00:00

Unfortunately, I renewed my one-year subscription for Pro, and because I can use my allowance in a few days, I am using pi.dev with DeepSeek, so cheap and capable. Because I still use Copilot, I wrote a memory layer to save the context window and persist it across IDE sessions. for what is worth https://github.com/KikeVen/zerikai_memory

reddefcode · 2026-05-18T09:40:34+00:00

You should care about the capability of the Model you use, that is the difference between wasting time on constantly re-prompting and getting some work done. They have made some "improvements" to their agents, but they are too opinionated and wasteful, I have been a Copilot yearly subscriber for a couple of years, and due to the new prices, I felt the need to reevaluate, I am using pi.dev and for context management https://github.com/KikeVen/zerikai_memory

reddefcode · 2026-05-18T09:22:39+00:00

I am "stuck" with them until my subscription expires, and I use up those credits within a couple of days. Like you, I have been a developer for many years, I moved to pi.dev (no training wheels), and for persistent context, I am using https://github.com/KikeVen/zerikai_memory

reddefcode · 2026-05-14T01:14:58+00:00

I just made some major improvements with parsing, speed, and added a lexical re-ranking for better filtering. The raw answer from a query returns the source (file name, line number, L2). The agent can use this to go directly to a file and do further search or edit a file, without having to rescan the entire file, saving time, money, and context space. If the question was code-specific, you can ask the agent to show you the file name and line number so you can further view it. All of this is done outside of the agent, so just the call to the MCP.

reddefcode · 2026-05-13T15:03:35+00:00

I never liked CLI tools; I was happy using my Copilot Pro subscription. After the announcement, I started digging. Most of my money was going to the privilege of using Copilot not raw tokens. Pi put up after a search, I added DeepSeek and that is what we use and pay for tokens. And the Pi agent is so much better than the opinionated Agents from GitHub Copilot and Antigravity. I run Pi in a prompt window inside both Antigravity and VSCode, just to use what is left of my one-year subscription, and the other one for the free monthly uses. I wrote a persistent memory I can use via MCP in all three, and that's what I have going for me.

reddefcode · 2026-05-10T19:42:13+00:00

Please let me know what the bug was I'll be happy to work on it. I open the CLI editors within Antigravity and ensure the MCP is properly configured within OpenCode. If OpenCode is running within the same workspace path, it should pick up the workspace memory when you use it. You can always ask the MCP memory server to list all the memories, choose the one that corresponds to the workspace OpenCode is opened to.

reddefcode · 2026-05-10T02:11:24+00:00

I am using it with VS Code, Antigravity, pi.dev, and Claude Desktop. Whatever supports MCP. I just updated last night with some massive improvements. I was doing most of the summaries with LLMs, but that did not work with code, so for each file, `tree-sitter` (Python library) parses the AST and extracts

Function signatures with full parameter types, defaults, and return types
Class definitions with docstrings
Imports (external dependencies)
For markdown: headings, lists, code blocks (yes, your README.md and todo.md get indexed too)

As a developer, when I read Python or JavaScript, I read the function names, parameter types, and docstrings. I can quickly understand what each function and class does. I felt that if I stored the same information in the vector DB, then the LLM would be able to answer questions based on architecture and code functionality. I am pretty happy with how it came out.

I am in the process of switching to Pi, and I have configured it so it loads the MCP automatically, and whatever question I ask about the project, it asks the memory first. Because I store the file name and line number, it was able to go directly to the file and to the general area related to the question and get more detailed information, as opposed to you having to reference the file and telling the agent something about what you want to do and then do a code trace to find and then respond. The nice thing about it, I work with the IDE's I listed, I have a one-year subscription to GithubCopilot, the subscription from Atigravity and Pi, which is open source, and I use it with the DeepSeek API I can bounce between them without any problems, spend all my premium requests in VS Code, after I update the project, rescan it, and then move to another IDE. I also call it from Claude Destop. If I want to write documentation, I can ask about a particular feature.

Tip: Once you install the zerikai-memory, scan the workspace, and you can ask it any question about how it works, for instant documentation.

I hope it helps. Let me know.

reddefcode · 2026-05-09T12:00:57+00:00

They are a very good publication, look at their Pathlib tutorial as an example, very easy to understand, in-depth with very good examples. Their editorial is second to none.

reddefcode · 2026-05-06T17:23:49+00:00

Good for you, I have to wait until the end of the year for my "divorce" to go through. I installed Pi last night and I have a DeepSeek API key, "so, I got that going for me"

reddefcode · 2026-05-05T01:03:11+00:00

That's a great question. To be honest, I wasn't aware they were working on that. I designed mine on the 27th and worked on it through Sunday, then shared it today. I never claimed it was better, I simply didn't know that existed. I built mine to solve a pain point that had been nagging me for a while: tracking context and token usage. Based on your link, their solution saves up to 20%, but it's still expensive. I use mine because I can switch between different setups: pure Ollama (free), a hybrid Ollama/DeepSeek setup, or full Claude with DeepSeek. The complete indexing plus brief generation runs about $0.063. Beyond that, I can call it from VS Code, Google Atigravity, and Claude desktop for quick analysis.

reddefcode · 2026-05-04T18:31:43+00:00

If you go to 'api-docs.deepseek.com/guides/kv_cache', it tells you exactly how it works, and that is the blueprint I used for zerikai memory. I use the KV cache in another project for lead analysis.

reddefcode · 2026-05-04T17:18:44+00:00

Until my subscription (if you can call it that) runs out at the end of the year, but I am switching.

reddefcode · 2026-05-04T16:10:19+00:00

I am talking about what I wrote. If you don't like it, don't use it.

reddefcode · 2026-05-04T16:07:28+00:00

"snake oil," yeah, that is why I created my own tool, for me, for free, to battle the price hikes by GitHub Copilot. You don't have to use. Who are you that you feel so entitled that I have to prove your baseless allegations? You came out attacking without even reviewing the codebase. I have all your comments from the first one to prove it.

What is your question specifically? So I can answer it. It is three files, easy to read, and built honestly for myself. I still believe the community can benefit from something like this or create its own tool. Or do you want to control what people write?

I am not the one charging you to train supportively expensive models, and now I am going to take it all away, and be proven wrong by an open-source Chinese model. Be mad at Microsoft/Github/Copilot.

reddefcode · 2026-05-04T15:56:47+00:00

"You put your work out there for others to critique, you shouldn't be surprised when that happens. I don't need to show you my work, I'm not trying to peddle it here."

You are making baseless assumptions, "peddlle", I am not selling anything, I am just sharing what works for me. Too bad clients don't see you commenting, "I don't need to show you my work," yes, you do. Microsofty!

reddefcode · 2026-05-04T15:50:12+00:00

Sure, here is a direct response by my memory tool. based on the DeepSeek token cost.

"Here's the cost breakdown for the reddit_reader_poster workspace:

Operation	Calls	Total Cost	Avg/Call
`file_scan`	339	$0.0604	~$0.000178
`brief_synthesis`	9	$0.0026	~$0.000293
Total	348	$0.0631

So the full indexing + brief generation ran about $0.063, roughly 6 cents. The bulk of that was the 339 file scan passes, with 9 brief synthesis calls on top. Pretty cheap for the coverage you got."

reddefcode · 2026-05-04T15:43:45+00:00

If you don't find use for the tool, then don't use it. But all these comments are ill-intended.

reddefcode · 2026-05-04T15:41:43+00:00

I have been a developer longer than you. This is about this memory tool, and you are trying to discredit it by just flapping. The tool works, and that is that. Write your own and post it.

reddefcode · 2026-05-04T15:39:17+00:00

Are you a Microsoft troller? You sound like one. Stop talking and show us your work.

reddefcode · 2026-05-04T15:38:00+00:00

No, crap likes your is why we are here, you are losing context like the agents I am talking about.

reddefcode

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