Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →
Plandex Alternatives
Similar projects and alternatives to plandex
-
-
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
litellm
Call all LLM APIs using the OpenAI format. Use Bedrock, Azure, OpenAI, Cohere, Anthropic, Ollama, Sagemaker, HuggingFace, Replicate (100+ LLMs)
-
-
-
-
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
-
promptfoo
Discontinued Test your prompts. Evaluate and compare LLM outputs, catch regressions, and improve prompt quality. [Moved to: https://github.com/promptfoo/promptfoo] (by typpo)
-
-
-
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better plandex alternative or higher similarity.
plandex reviews and mentions
Posts with mentions or reviews of plandex.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-18.
-
Meta Llama 3
I'm building Plandex (https://github.com/plandex-ai/plandex), which currently uses the OpenAI api--I'm working on support for Anthropic and OSS models right now and hoping I can ship it later today.
You can self-host it so that data is only going to the model provider (i.e. OpenAI) and nowhere else, and it gives you fine-grained control of context, so you can pick and choose exactly which files you want to load in. It's not going to pull in anything in the background that you don't want uploaded.
There's a contributor working on integration with local models and making some progress, so that will likely be an option the future as well, but for now it should at least be a pretty big improvement for you compared to the copy-paste heavy ChatGPT workflow.
-
Anthropic launches Tool Use (function calling)
I'm looking forward to trying this out with Plandex[1] (a terminal-based AI coding tool I recently launched that can build large features and whole projects).
Plandex does rely on OpenAI's streaming function calls for its build progress indicators, so the lack of streaming is a bit unfortunate. But great to hear that it will be included in GA.
I've been getting a lot of requests to support Claude, as well as open source models. A humble suggestion for folks working on models: focus on full compatibility with the OpenAI API as soon as you can, including function calls and streaming function calls. Full support for function calls is crucial for building advanced functionality.
1 - https://github.com/plandex-ai/plandex
-
Show HN: Plandex – an AI coding engine for complex tasks
The server does quite a bit. Most of the features are covered here: https://github.com/plandex-ai/plandex/blob/main/guides/USAGE...
I actually did start out with just the CLI running locally, but it reached a point I needed a database and thus a client-server model to get it all working smoothly. I also want to add sharing and collaboration features in the future, and those also require a client-server model.
-
Discovering Devin, Devika, and OpenDevin
In my opinion, an "AI software engineer" is the wrong target for the current generation of models, though it's obviously good for generating buzz.
I'm working on an open source, OpenAI-based tool that uses agents to build complex software (https://github.com/plandex-ai/plandex), and I have found that the results are generally much better when you target 80-90% of a task and then finish it up rather than burning lots of time and tokens on trying to get the LLM to do the entire thing.
Results are also better, in my experience, when the developer remains in the driver's seat--frequently stopping, revising prompts and context, and then retrying rather than expecting to kick back and let the model do everything.
-
How well can LLMs write COBOL?
This is interesting. I'm working on an OpenAI-based tool for coding tasks that are too complex for ChatGPT - https://github.com/plandex-ai/plandex.
It's working quite well for me, but it definitely needs some time spent on benchmarking and ironing out edge cases.
I'm especially curious how it will do on more "obscure" languages. Not that Cobol is obscure exactly--I suppose there's probably quite a bit of it in GPT-4's training considering how pervasive it is in some domains. In any case, I'll try out this benchmark and see how it goes.
-
Ask HN: Is anybody getting value from AI Agents? How so?
I'm working on an agent-based tool for software development. I'm getting quite a lot of value out of it. The intention is to minimize copy-pasting and work on complex, multi-file features that are too large for ChatGPT, Copilot, and other AI development tools I've tried.
https://github.com/plandex-ai/plandex
It's working quite well though I am still working out some kinks.
I think the key to agents that really work is understanding the limitations of the models and working around them rather than trying to do everything with the LLM.
In the context of software development, imo we are currently at the stage of developer-AI symbiosis and probably will be for some time. We aren't yet at the stage where it makes sense to try to get an agent to code and debug complex tasks end-to-end. Trying to do this is a recipe for burning lots of tokens and spending more time and than it would take to build something yourself. But if you follow the 80/20 rule and get the AI to the bulk of the work, intervening frequently to keep it on track and then polishing it at the end, huge productivity gains are definitely in reach.
-
A note from our sponsor - InfluxDB
www.influxdata.com | 29 Apr 2024
Stats
Basic plandex repo stats
12
8,826
9.7
13 days ago
plandex-ai/plandex is an open source project licensed under GNU Affero General Public License v3.0 which is an OSI approved license.
The primary programming language of plandex is Go.
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com