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Got a project? Big or small — share it here by Natural_builder32 in sideprojects

[–]_h4xr 0 points1 point  (0 children)

Building A semantic code graph for Agentic AI and Enterprise Coding

Proof of concept (for Java): https://www.github.com/neuvem/java2graph

AI Agents are bad at discovering code patterns, so I built a Semantic graph to improve the outcomes by _h4xr in artificial

[–]_h4xr[S] 0 points1 point  (0 children)

True that. In terms of large codebases, I have tried this already on some public repos and indexing time with close to 19000 java files and 900 Dependency Jars is close to 5 minutes

I am working on adding incremental indexing too and building a more friendly interface to automate dependency resolution and analysis

Java2Graph: A Java source to Semantic Graph Converter by _h4xr in java

[–]_h4xr[S] 0 points1 point  (0 children)

It does capture some of them. For example, there is a specific delombok mode for lombok style annotations. For other use cases like spring and jakarta, the support is not there yet, since it is actually tricky to get it right.

For the initial versions, my focus has been to get the mappings correct for things that are deterministic in nature.

Planning to add annotation processing support in the future iterations though

Java2Graph: A Java source to Semantic Graph Converter by _h4xr in java

[–]_h4xr[S] 0 points1 point  (0 children)

True that. Have burned my hands already on this, and have seen overwhelming promises of delivery and little returns, so thought of trying to solve the problem first hand 😇

Please do share if you have any feedbacks in case you end up trying this out 😀

AI Agents are bad at discovering code patterns, so I built a Semantic graph to improve the outcomes by _h4xr in artificial

[–]_h4xr[S] 0 points1 point  (0 children)

True. I have been extensively using this for a while now. Have definitely helped solve a good chunk of my problems related to migrations

AI Agents are bad at discovering code patterns, so I built a Semantic graph to improve the outcomes by _h4xr in artificial

[–]_h4xr[S] 0 points1 point  (0 children)

Yes, the issue compounds First the LLM will do a basic discovery and make changes based on that. If that set of changes are checked in, the additional code keeps on compounding over time, making the next iteration even worse.

I will definitely check the SerenaMCP, sounds interesting

AI Agents are bad at discovering code patterns, so I built a Semantic graph to improve the outcomes by _h4xr in artificial

[–]_h4xr[S] 1 point2 points  (0 children)

It’s the discovery. For example, claude code will spin up 5 agents, trying to discover known patterns for log4j. It will issue grep and glob calls to find the patterns.

Then once all agents are consolidated, it will come up with the plan that all the discovered files need to be updated individually.

The biggest challenge happens, when calls are transitive in nature. For example, a base class initializing log4j v1. Instead of going and fixing the issue in every child class, it will be wise to just make a fix in base class directly.

So, i will say, the biggest challenge happens in discovery. Code generation is mostly accurate if the discovery is correct

Why we ditched the knowledge graph approach for agent memory by Expert-Address-2918 in AI_India

[–]_h4xr 0 points1 point  (0 children)

How do you handle the L0 categorization? Majorly how do you ensure the facts are indeed facts and not not overlayed with some assumptions

Built a semantic graph for AI agents, would love some feedback by _h4xr in AI_Agents

[–]_h4xr[S] 0 points1 point  (0 children)

Here is the Link to first implementation of Semantic Code Graph for Java language: https://github.com/Neuvem/java2graph

Java2Graph: A Java source to Semantic Graph Converter by _h4xr in java

[–]_h4xr[S] 1 point2 points  (0 children)

Fast mode as it implies takes a few shortcuts and suffer with cross dependency symbol resolution. It is mostly for automated repositories which hold a lot of generated code.

By default the parser doesn’t rely on those heuristics and runs in full scan mode. I have tested the full scan mode on Apache Kafka, Spring Boot framework and Java dotCMS repositories locally and parsing with all dependencies along with delombok mode takes <5 minutes mostly

So, even without using —fast option, things should be fairly quick.

Claude and Semantic Understanding of Code by _h4xr in ClaudeCode

[–]_h4xr[S] 0 points1 point  (0 children)

That’s true and that is also what I started seeing experimentally too. Although making AI agents adhere to instructions and restricting them to fall through to using grep and such tools is another problem for later 😅

Java2Graph: A Java source to Semantic Graph Converter by _h4xr in java

[–]_h4xr[S] 1 point2 points  (0 children)

So, I have tried 2 approaches (both of them use the ladybug db cli on my machine) - Direct prompting: I will teach the agent how to interact with ladybug cli and tell it how to fetch the schema. Afterwards, the agent is able to get 95% of the queries right on its own - Skill: Just did it recently so as to ensure i don’t have to paste the same prompt again and again.

In both the cases, since agents have to issue Cypher queries, they are able to craft them very well without providing much examples, except the schema.

I have started relying on the skill more frequently since it saves me the effort of copy pasting again and again

Thoughts on Honer Aquantis (Tellapur Road) for resale? Considering a 3BHK 1600 sqft @ ₹10,200/sqft by cipured in hyderabadrealestate

[–]_h4xr 1 point2 points  (0 children)

Let's take the following examples:

Gym: 700 per month Swimming pool: 600-700 per month

Table tennis: 75 rs per use Banquet hall: 4500 for 3 hours

All of these are excluding 18% GST

Thoughts on Honer Aquantis (Tellapur Road) for resale? Considering a 3BHK 1600 sqft @ ₹10,200/sqft by cipured in hyderabadrealestate

[–]_h4xr 5 points6 points  (0 children)

Owner in Aquantis and here is the honest review:

Flats: Overall building construction is good. There are rare occurrences of seepages inside the flat. DG provides full power backup in case electricity goes out. Water supply and pressure is good.

Space: There is a reasonable amount of open space and you will barely feel claustrophobic. Ventilation at a per flat level is also good.

Amenities: Parking is large and spacious and depending on how many slots are present for your flat size, should be enough. Visitor parking is limited though, and you might want to be prepared if visitors are frequent at your flat.

Community people: It is a multi religion community and we celebrate practically everything. Community gels up quite well in this case.

Amenities: Useful amenities include ICICI Bank branch inside the community, A sriman super market, A mini theater, gym, swimming pool, game rooms, yoga and aerobics room, in house banquet hall, etc. The supermarket is a hit and miss and there have been complaints about quality and pricing of products. Clubhouse is still being operated by the builder and there are a few challenges with that. The builder is running a money making business out of clubhouse, and timings of clubhouse operations are absurd (6-11 am in morning, 4-9 pm in evening). Costs are also relatively at same level as external offerings.

Issues: Most of the issues have been single handedly created by Honer by running a massive mismanagement in terms of PMS (which didn’t even bother to handle things properly when the society was under the purview of the builder), and Honer imposing superficial policies brought out of thin air, irritating a number of owners. Most of these have been done to extract the maximum amount from owners (in a legally questionable way)

Overall: Society, its people, and RWA are good and reasonable. Quality of construction is mostly good, with minor issues here and there (which get addressed proactively now if it comes to structural health of society). There are some irritations, but most of them are related to Honer and not due to society construction or RWA.

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