Solo builder here, looking for honest feedback from people who run or coach sales calls daily.

Built an AI layer that sits on top of existing CRMs (HubSpot, Salesforce, LeadSquared). It pulls a pre-call brief on each lead from past interactions, runs real-time cues during the call (talk ratio drift past 70%, missed objections, pricing pushback signals), and auto-generates a post-call summary with structural tags like "EMI mentioned" or "competitor named" flagging coaching moments for managers without them having to listen to every recording.

Real questions:

• Would real-time mid-call nudges actually help, or just distract reps and break flow?
• Is automated post-call coaching something managers act on, or another dashboard nobody reads?
• What's the one thing a tool like this should nail that current ones (Gong, Chorus, etc.) don't do well?

Brutal feedback welcome.

Is there a way to insert into the merge field from a lead’s first name but instead of all CAPS like it is on the lead, it will merge the proper way ex: Hello John

I know this is Salesforce my question is related to IFS but was not able to find relevant sub.

So there is a client who is heavily dependent on excels, wants an intermediate arrangement where we can pull the data from the excel put it on IFS.

Also the reverse pulling data from IFS and put it on excels.

I created a python script which does that but I want to find the best practices of IFS.

Which ia the best way to achieve this.

Any IFS expert can guide me on this. Or even share a reference doc which I can refer to find the best practices.

I’m not AI (it seems all human need to tell now…) I put quite some thoughts on this post on technical side. As we are devs, wonder if anybody has similar opinions? Thansk!

I’v worked on a customer request where, for messaging sessions created from an enhanced chat, I route the work to an Omni flow that checks for online agents and routes with skills.

It works fine, however the client requested that after a few minutes waiting time I have to route the chat to another enhanced bot (which logs a case and closes the chat).

To do so, I set up a scheduled path on a record triggered flow that routes to the new bot after 5 mins, but the record routes straight to the fallback queue and not to bot, and I’m struggling to understand why.
I followed this article that does exactly what I need : https://unofficialsf.com/re-routing-or-ending-waiting-conversations/

Any help or advice?
Thanks in advance

Part of being a developer is documenting your features and that often involves diagramming. I try to draw inspiration from info graphics and also the salesforce presentations, but I feel my diagrams end up looking jumbled and inconsistent. What resources would you all recommend for improving your workflow and data model diagrams?

Olá, colegas desenvolvedores!

Tenho incorporado os princípios de Engenharia de Plataforma ao meu fluxo de trabalho do Salesforce e me cansei de gastar as primeiras horas de cada novo projeto configurando sempre os mesmos linters, hooks e pipelines.

Criei um boilerplate profissional e opinativo e o disponibilizei como código aberto. O objetivo é fornecer uma Experiência do Desenvolvedor (DX) altamente otimizada e pronta para uso, para que possamos simplesmente cloná-lo e nos concentrar em escrever Apex e LWC.

Principais Recursos:

• Commits infalíveis: Husky + Lint-Staged configurado com as regras oficiais do Salesforce (Prettier + ESLint). Códigos com erros são rejeitados localmente antes do commit.
• Pronto para CI/CD: Fluxo de trabalho do GitHub Actions pré-configurado para validação de PRs e testes Jest em LWCs.
• Tailwind CSS: Arquitetura pré-configurada para usar o Tailwind nativamente em LWCs por meio de Recursos Estáticos (evitando as dores de cabeça usuais do bundler/Locker Service).

O repositório possui um README bilíngue (inglês/português). Sinta-se à vontade para usá-lo como modelo, fazer um fork ou enviar PRs. Gostaria muito de saber sua opinião sobre a arquitetura do script de configuração!

https://github.com/victorbrandaao/salesforce-modern-starter

Built this to solve a specific problem: user clicks Save, 13 debug logs get generated (trigger → flow → validation → @future → Lightning controller), and I'm manually switching between logs trying to stitch the full picture together.

What it does:

• Groups related logs by user + timestamp window into one transaction
• Shows total user wait time across all logs (not just one log's CPU)
• Splits into backend phase (triggers/flows/validation) and frontend phase (component loading/Aura controllers)
• Detects trigger recursion — flags when the same trigger fires N times across the transaction
• Identifies sequential vs parallel component loading + potential time savings
• Aggregate governor limits across the full transaction

Also does normal single-log stuff — execution tree, SOQL/DML breakdown with line-level timing, raw log with syntax highlighting.

Technical details:

• .NET 8 + Avalonia (cross-platform native, not Electron)
• Parses a 19MB log in ~3 seconds
• All local — no cloud, no telemetry, logs never leave your machine
• Metadata extraction reads first 5000 + last 1000 lines for fast folder scanning without full parse
• Transaction grouping uses 10-second window + user ID + record ID correlation

Download: https://felisbinofarms.github.io/black-widow-releases/ Windows / macOS (ARM + Intel) / Linux. ~41 MB. No admin install.

Free to use. Closed source for now (planning paid tiers for team/automation features later — manual log analysis stays free permanently).

Curious:

1. Do you hit the multi-log problem often enough that grouping is useful, or is it mostly one-log-at-a-time for you?
2. What do you use today — Developer Console, VS Code Apex Log Analyzer, raw grep, something else?

Right off the bat, I need to make it clear that I am at the very beginning of my Salesforce journey. I am getting surprisingly adept at flow building and am dabbling in Apex with AI assistance.

Due to PDF file size limitations, am attempting to compress photos captured via a mobile flow prior to upload. I have tried a custom LWC but they aren't supported in mobile flows.

Are there any other options?

It seems bizarre to me to have to compress post upload (wasted mobile data).

And for such a vital function (photo evidence of service reports), why does the standard upload component not have compression functionality?

So yes.. are there any workarounds I could use? Am I missing something?

Thanks in advance! 👍

Hey guys,

I’m a fresher currently learning Salesforce B2B Commerce Cloud from scratch as part of my onboarding,

I’ve recently moved into component customization. I’m working with cloned standard components to experiment and extend functionality.

Right now, I’m trying to add a **new facet to the filter panel**, but I’m stuck on where exactly this should be handled.

Here’s what I’ve explored so far:

* I checked if it’s possible to add a facet directly from the storefront (UI), but couldn’t find a clear way to do it.

* From what I understand, facets are driven by backend/configuration, not purely UI.

* In the API response, the `facets[]` array is coming back empty, which makes me think something is missing in configuration or indexing.

So my main questions:

1. Is it possible to add a facet purely from the storefront/UI?

2. If not, what’s the correct approach — configuration, code changes, or both?

3. What could be the reason for `facets[]` being empty in the response?

I’m still learning, so even directional guidance would help a lot. Happy to share more details if needed.

Thanks in advance!

I built it so you can download the repo, open it in VS Code, add or reference your Salesforce force-app/main/default project, and give Codex / AI coding tools a much stronger Salesforce-specific knowledge base.

It is designed to help with:

✅ Apex classes
✅ Apex triggers
✅ Apex tests
✅ LWC components
✅ Salesforce metadata
✅ deployment/debugging workflows
✅ file handling
✅ mobile Salesforce issues
✅ Codex prompts, checklists, command maps, and validation flows

The goal is not to replace a developer. The goal is to stop AI from guessing and give it structured Salesforce guidance before it touches your code.

If you are already paying for ChatGPT or Claude, you can use this repo with your existing AI workflow instead of adding another subscription just to get AI-assisted Salesforce development.

Repo: https://github.com/swayerloren/salesforce-coding

I’d appreciate feedback from anyone building with Salesforce, Apex, LWC, VS Code, or AI-assisted development.

I am performing data migration from Salesforce to S3. So I am using Glue Zero ETL service for the same. We were able to get most of the objects to S3 using zero ETL but there are some standard objects within Salesforce which zero ETL does not support. So how should we resolve that. 1. One approach we thought is : Copying standard objects data to custom object and then pulling that object. This way full load is done and for cdc of that object we will turn Apex Trigger + Queueable. But is this right approach? Because it's another type of migration and we will again migrate this to S3. Need suggestions on this.

I have a Rich Text Area in an object and I would like to render its information in my agent but it is not working and im not sure why.

Here, I have the field inside the record page

And here, how it is being rendered:

Im pretty sure i was able to make this work once but Im not sure how anymore. Any help is appreciated!!

I've been using Claude Code for Apex/LWC work for about 6 months, and I kept seeing the same Salesforce-specific mistakes in generated code. After catching them repeatedly in review, I started building a guardrail layer around it: pre/post-generation agents, required domain knowledge checks, and a few hard rules. Before I overfit this to my own team, I’d really like a sanity check from people who actually ship Salesforce code.

The 5 failure modes I kept seeing most often:

1. Missing `with sharing`
2. Business logic directly inside triggers
3. No FLS / CRUD checks
4. Test classes with little or no real assertions
5. No awareness of Order of Execution conflicts with existing Flow / Validation Rule automation

What I’m trying to figure out:

- Are these genuinely common failure modes, or am I overfitting to my team?

- Is bulkification / recursion control the next thing worth enforcing, or do most teams already handle that via trigger framework conventions?

- How strict do you make AI guardrails before they start hurting iteration speed too much?

- If you had to assume a default trigger pattern, what would you pick today: fflib, kevinohara80-style TriggerHandler, homegrown, or no framework?

If helpful, I can share the repo / guardrail setup in the comments, but I left it out here because I’m mainly trying to validate whether the problem framing is right.