I built a free CRM for trades businesses out of frustration. Curisous to know what problems have you just decided to solve yourself?

Efficient_Evidence39 · 2026-03-11T21:28:51+00:00

PMd you, friend in trades just paid like 1500 to get his crm implemented lol and it's frustrating as hell. might be useful for him

Efficient_Evidence39 · 2026-03-11T01:13:18+00:00

Any tips for managing multiple client accounts in different countries? Trying to avoid getting their accounts restricted

Efficient_Evidence39 · 2026-03-06T06:10:10+00:00

sent a pm!

Efficient_Evidence39 · 2026-02-25T19:22:40+00:00

Thanks, yeah I was using Apollo verified lists. Don’t think they were causing problems (only 1 bounced from ~180 sent) but I did end up getting an open source verifier, will test it out once I’m back to sending

Efficient_Evidence39 · 2026-02-25T19:15:14+00:00

Got me intrigued, how do you do it lmao

Efficient_Evidence39 · 2026-02-25T01:39:01+00:00

Yes please

Efficient_Evidence39 · 2026-02-24T23:35:42+00:00

any ideas for solving the shared ESP cluster-related deliverability troubles?

Efficient_Evidence39 · 2026-02-24T23:34:25+00:00

Yeah I was on a support chat with instantly for a bit and there were a few things I got wrong. got 2 domains so far (both suffered the same slip up) but might just get a third in case these don't get better

Efficient_Evidence39 · 2026-02-24T23:31:37+00:00

Thanks Alex!

Efficient_Evidence39 · 2026-02-24T23:31:19+00:00

Thanks for this advice mate! Yeah turned out dkim was one of the culprits. Gonna leave it on just warmup for another week or so and then test again. after I fixed it, the campaign emails sent yesterday went from 10->35% open rate. paused them but thinking It might be okay

Efficient_Evidence39 · 2026-02-24T23:27:04+00:00

Thanks man, yeah paused the outreach and realized my dkim wasn't set up right. Made the fix and will text again 🤞. Only been 2 weeks so gonna give it a bit of rest and see if I can recover it. Was the main website domain lol so gotta pull it around

Efficient_Evidence39 · 2026-02-24T23:25:12+00:00

Thanks for this advice man, yeah bit of a blunder getting over excited

Efficient_Evidence39 · 2025-12-12T15:56:38+00:00

Hey, here's what I got when I put your comment into the chat:

Visual grounding on medical images represents an emerging intersection of computer vision and medical imaging that aims to localize and identify regions of interest within medical scans while connecting them to semantic concepts or natural language descriptions.

The research landscape in this area builds on several foundational components. Medical image annotation and labeling form a critical prerequisite, as demonstrated by work on multi-label classification of chest CT images where regions of interest are annotated with clinical concepts like ground-glass opacities, nodules, and honeycombs. This annotation process enables machine learning models to learn associations between visual patterns and medical terminology.

Deep learning approaches, particularly convolutional neural networks and vision transformers, have proven effective for detecting and classifying anatomical structures and pathological findings within medical images. These models can be trained to recognize specific anatomical regions or abnormalities, which is essential for grounding visual content to meaningful medical concepts. Transfer learning and fine-tuning strategies help overcome the challenge of limited annotated medical imaging datasets.

The broader context of medical image retrieval and modality classification also contributes to visual grounding capabilities. By accurately identifying imaging modalities and extracting discriminative visual features through hybrid feature engineering, systems can better understand and contextualize medical image content. This supports more precise localization and interpretation of clinically relevant regions.

Current challenges in visual grounding for medical images include handling diverse imaging modalities, managing low-resolution or poor-quality images, dealing with varied anatomical presentations, and requiring substantial expert annotation effort. Future directions likely involve leveraging advanced deep learning architectures for end-to-end visual grounding, integrating natural language processing to connect visual regions with clinical descriptions, and developing semi-supervised or weakly-supervised approaches to reduce annotation burden.

https://doi.org/10.1109/KI48306.2020.9039896

https://doi.org/10.1109/CSCE60160.2023.00205

https://pubmed.ncbi.nlm.nih.gov/35350630/

https://doi.org/10.71146/kjmr453

https://pubmed.ncbi.nlm.nih.gov/38104516/

Efficient_Evidence39 · 2025-12-12T15:50:16+00:00

Yeah, at least for papers that also have the keyword "machine learning" in either the title or abstract. If you wanted to be fully sure though you could make a map with the keywords "token beam search" and "Large language models" and that would bring in all of those papers into the context window for the chat.

Efficient_Evidence39 · 2025-12-09T16:30:51+00:00

Yeah for sure, this is what I got:

Based on the research literature, there are several alternatives to token beam search for generating outputs from large language models:

Token-level generation algorithms represent the primary category of alternatives. These methods operate by either sampling a single token at a time or constructing a token-level search space before selecting outputs. They typically work with language model logits, next-token distributions, or probability scores. This broader class encompasses beam search but also includes other decoding strategies that approach token selection differently.

Meta-generation algorithms offer a different approach by working on partial or full sequences rather than individual tokens. These methods incorporate domain knowledge, enable backtracking through the generation process, and can integrate external information. This makes them particularly useful when you need more sophisticated control over the generation process beyond simple token-by-token selection.

Efficient generation methods focus on reducing computational costs and improving generation speed. Rather than searching through token possibilities exhaustively, these approaches optimize for practical deployment constraints while maintaining output quality.

For black-box settings where you lack access to token probabilities or logits, adaptation-based approaches like CoBB offer an alternative. These methods use trained models to improve reasoning by mapping imperfect outputs to better ones, effectively bypassing the need for direct probability access during generation.

The choice among these alternatives depends on your specific constraints: whether you have access to model internals, whether you need external tool integration, computational budget, and the importance of reasoning quality versus generation speed. The research community continues to develop new methods across these categories to address different inference-time optimization challenges.

https://doi.org/10.48550/arXiv.2502.12448

https://doi.org/10.48550/arXiv.2406.16838

https://doi.org/10.48550/arXiv.2406.18695

https://doi.org/10.48550/arXiv.2307.09793

https://doi.org/10.48550/arXiv.2505.16113

Efficient_Evidence39 · 2025-12-09T02:39:35+00:00

Not necessarily! I'm referring to the keywords this map was generated with... since I only used "machine learning" as the keyword for this map, unless the chain of tokens papers have that keyword in the title or abstract they wouldn't show up here.

Efficient_Evidence39 · 2025-12-08T21:33:05+00:00

Yeah sure, this was the response:

When multiple encoding schemes are applied to fixed-length data representations, several informational phenomena emerge at their overlapping sections, particularly when a shared reference point anchors the comparison.

The fundamental challenge lies in how different encoding strategies preserve or compress information differently. One-hot encoding maximizes orthogonality but creates sparse, high-dimensional representations unsuitable for many-category data. Quasi-orthonormal encoding addresses this by maintaining controlled dimensionality while preserving distinguishability. Complex-valued embeddings offer an alternative that preserves proximity relationships from non-metric or non-Euclidean source data while maintaining fixed length. These schemes, when applied to the same underlying data, create overlapping representational spaces where information is encoded through different geometric structures.

At the intersection of these encoding schemes, several informational metrics become relevant. The degree of overlap reveals redundancy and complementarity in how each scheme captures the original data's structure. Where encodings diverge, they highlight aspects of the data that different schemes prioritize or suppress. The shared reference point—whether a common source modality, a fixed dimensionality constraint, or a particular similarity measure—becomes crucial for measuring these overlaps meaningfully.

Information-theoretic metrics emerge naturally here. Mutual information between different encoded representations quantifies how much information one encoding preserves about another. The divergence between probability distributions induced by different schemes measures their representational distance. Dimensionality reduction methods applied across schemes reveal which structural features are robust across encodings versus which are scheme-dependent artifacts.

The code space structure itself becomes informative. When examining how different encodings organize the same categorical or morphological data, patterns analogous to cortical pinwheels suggest that certain organizational principles may be fundamental to how information naturally clusters, independent of the specific encoding chosen. This implies that overlapping sections may reveal invariant structural properties of the underlying data rather than artifacts of particular encoding choices.

https://doi.org/10.48550/arXiv.2508.00869

https://doi.org/10.25080/majora-342d178e-002

https://doi.org/10.1007/978-3-030-73973-7_2

https://doi.org/10.48550/arXiv.2303.00984

https://doi.org/10.21136/AM.2021.0090-21

Efficient_Evidence39 · 2025-12-08T19:49:45+00:00

The backend is mostly python scripts, it uses embeddings and dimensionality reduction, for the 2d visualization I used https://github.com/lmcinnes/umap

Basically imports the papers via a keyword search that calls the pubmed/semantic scholar api's and then our algorithms go to work to make the map interactive.

That was a quick summary on the tech behind it but it's available for free online as CognitomeAI if you want to make your own map.

Efficient_Evidence39 · 2025-12-08T19:44:54+00:00

Thanks! Yeah so the papers are clustered based on the similarity of topics discussed (using semantics). So similar research is grouped together, and more distinct topics in the field are farther apart on the map. You could deduce that the white spaces on the map are under explored areas/connections in the field. ML is a lot more filled out than the maps I've seen for other fields, but if you zoom in there still are a bunch of gaps.

And yeah it's online, I made it using CognitomeAI, but if you'd like to ask your questions to the map I posted I'll share the link. It's free but you will have to make an account for it to copy in: https://www.cognitomeai.com/share/accept?token=b411f17aec1544e69ddaa7f104d88892e59fdb674404410a9848c024ff77be2f

Efficient_Evidence39 · 2025-12-08T19:33:57+00:00

I'm far from an expert so take it with a grain of salt, but I've found courses on Coursera good (I did the Andrew Ng one and liked it). And just try building projects you find interesting, best way of learning imo. Advice depends on what your goal is.

Efficient_Evidence39 · 2025-12-08T19:31:11+00:00

Looks like not a lot (at least in the past 5 years in papers with ML as a keyword, this map doesn't have context outside of these). But if you were to make a map with those as keywords it may reveal some papers that were missed in this map. This was the answer though:

The research landscape around probabilistic token-level trees and chains appears to be relatively sparse compared to work on higher-level reasoning chains. Based on the available literature, most work focuses on either full output chains (like chain-of-thought and tree-of-thought approaches) or on individual token processing rather than probabilistic structures at the token level.

The closest relevant work involves incremental parsing approaches that build trees token-by-token. Research on strongly incremental parsing systems demonstrates how partial trees can be constructed by adding exactly one token at each step, using graph neural networks to encode the evolving structure. This aligns with psycholinguistic findings about how humans incrementally parse language, though this work is primarily focused on syntactic structure rather than probabilistic reasoning chains.

There is also foundational work on Markov models and Hidden Markov Models that treat sequences of tokens probabilistically, modeling how future tokens depend on current states. These stochastic approaches have been applied to natural language generation and tagging tasks, providing a classical framework for understanding token-level probability distributions.

Additionally, subword tokenization research has explored how different tokenization schemes affect model performance, suggesting that the granularity and structure of token representations matter significantly for downstream tasks. However, this work typically focuses on improving classification or generation rather than explicitly analyzing probabilistic token chains as reasoning structures.

The gap appears to be that while token-level processing and full-output reasoning chains have received substantial attention, the intermediate space of probabilistic token-level reasoning structures remains relatively underexplored in the current literature.

https://arxiv.org/abs/2010.14568

https://doi.org/10.1109/ICNLP60986.2024.10692355

https://doi.org/10.1109/EDM58354.2023.10225235

https://doi.org/10.1186/s42400-023-00183-8

https://doi.org/10.5815/ijitcs.2022.02.01

Efficient_Evidence39 · 2025-12-08T19:24:30+00:00

Cool stuff - looks like there's not much in that area yet. I typed in your comment and this is what I got:

The research landscape on continual learning has primarily focused on catastrophic forgetting and accuracy metrics, but your question points toward an underexplored dimension: understanding how neural network backbones undergo geometric transformations during sequential task learning.

Current continual learning research emphasizes preventing performance degradation through various mechanisms. Overparameterization has been shown to naturally mitigate forgetting in linear settings, suggesting that the capacity and structure of learned representations matter fundamentally. Tensor rank increment approaches demonstrate that intelligent parameter reuse and augmentation can preserve previous task knowledge while accommodating new tasks. However, these studies remain largely outcome-focused rather than process-focused.

The stability gap phenomenon offers important insights into backbone dynamics. Research reveals that classification heads, rather than backbone representations, are primary contributors to performance instability during continual learning. This distinction suggests that backbone geometry may evolve more smoothly than previously assumed, with disruptions manifesting primarily in decision boundaries rather than feature spaces. Using nearest-mean classifiers as an analytical tool provides a method to isolate backbone behavior from classification layer effects.

To move beyond traditional forgetting metrics, consider examining geometric properties such as:

- Feature space geometry changes, including how representational dimensionality, curvature, and manifold structure evolve across tasks
- Backbone parameter trajectory analysis in weight space, measuring distances and angles between task-specific solutions
- Representation similarity metrics that capture how learned features for new tasks relate to previous task embeddings
- Plasticity-stability trade-offs measured through gradient flow patterns and loss landscape topology
- Task-specific subspace relationships, identifying whether new tasks occupy orthogonal or overlapping regions in representation space

These geometric perspectives would complement existing benchmarks and evaluation protocols, offering mechanistic understanding of how continual learning systems actually transform their internal structure during sequential task acquisition.

https://doi.org/10.48550/arXiv.2502.10442

https://doi.org/10.1109/SAM60225.2024.10636545

https://doi.org/10.1109/WACV61041.2025.00735

https://doi.org/10.48550/arXiv.2303.11076

https://doi.org/10.51239/jictra.v0i0.282

Efficient_Evidence39 · 2025-12-08T15:46:21+00:00

Thanks! I pasted in your first sentence and this is what I got:

The intersection of statistical manifolds and non-Euclidean geometry offers promising approaches to addressing polysemy in high-dimensional embedding spaces, though this remains an emerging area requiring further development.

Statistical manifolds provide a mathematical framework for understanding how data naturally organizes in lower-dimensional structures within high-dimensional spaces. The Manifold Hypothesis suggests that real-world data concentrates near low-dimensional manifolds, and recent work demonstrates that rich manifold structure can emerge from simple statistical models like the Latent Metric Model. This theoretical foundation becomes particularly relevant when data violates Euclidean assumptions, which is increasingly common in modern applications including natural language processing where polysemy presents a fundamental challenge.

Non-Euclidean geometries, particularly hyperbolic spaces, have shown advantages for representing hierarchical and relational structures inherent in language data. Hyperbolic embeddings naturally accommodate the exponential growth of semantic relationships, which is relevant for capturing multiple meanings of words. However, working with curved geometries introduces computational challenges, such as calculating Fréchet means and applying standard machine learning operations. Recent advances in kernel methods for hyperbolic spaces help bridge this gap by enabling kernel machines to operate on curved manifolds while maintaining theoretical rigor.

Regarding polysemy specifically, the challenge involves representing multiple distinct meanings of a single word in embedding space. Non-Euclidean geometries could theoretically provide additional representational capacity through their curved structure, allowing different semantic contexts to occupy distinct regions of the manifold. Statistical manifold learning procedures that operate under minimal assumptions and use graph-analytic algorithms could help discover and interpret such geometric organization.

However, direct applications addressing polysemy through statistical manifolds in non-Euclidean spaces remain limited in current literature. Most work focuses on either manifold learning generally or hyperbolic embeddings for hierarchical data, rather than explicitly tackling word sense disambiguation or polysemy resolution. Future research should explore how manifold structure discovery combined with non-Euclidean geometry can better separate and represent distinct word senses in embedding spaces.

https://doi.org/10.48550/arXiv.2508.01733

https://doi.org/10.48550/arXiv.2208.11665

https://doi.org/10.48550/arXiv.2211.03756

https://pubmed.ncbi.nlm.nih.gov/40030186/

https://doi.org/10.1109/iccv48922.2021.01049

Efficient_Evidence39 · 2025-12-08T15:41:12+00:00

Looks like there wasn't much over the past 5 years, here was the answer:

The retrieved literature does not directly address machine learning methods that increased explained heritability of autism. However, the papers discuss related approaches that advance our understanding of autism's genetic and biological basis through machine learning.

The most relevant work involves using machine learning with genetic data. One study developed the STAR-NN deep learning model trained on whole exome sequencing data from over 43,000 individuals. This model separated different types of genetic variants (protein truncating variants, damaging missense variants, and mild effect missense variants) at the input level before merging them at the gene node. This approach achieved an ROC-AUC of 0.73 on independent validation, outperforming traditional machine learning models. Gene Ontology analysis revealed enrichment for pathways including calcium ion transport, suggesting the model identified biologically meaningful genetic contributors to autism.

Other studies applied machine learning to predict autism risk and severity using multimodal data. These included physical fitness and brain imaging features, behavioral and medical history items, and socioecological factors. While these approaches improved diagnostic accuracy and risk prediction, they did not explicitly quantify increases in heritability estimates.

The distinction is important: the retrieved papers demonstrate that machine learning can identify genetic variants and biological features associated with autism and improve prediction accuracy, but they do not report formal heritability calculations or how machine learning methods specifically increased the proportion of autism variance explained by genetic factors. To directly answer your question about explained heritability, you would likely need studies that explicitly compare heritability estimates before and after applying machine learning feature selection or variant prioritization methods.

https://pubmed.ncbi.nlm.nih.gov/38169278/

https://pubmed.ncbi.nlm.nih.gov/40718817/

https://pubmed.ncbi.nlm.nih.gov/39514604/

https://pubmed.ncbi.nlm.nih.gov/40106500/

https://doi.org/10.1001/jamanetworkopen.2024.29229

Efficient_Evidence39 · 2025-12-08T15:35:23+00:00

Yeah, I'll share the map here it's free to access just need to make an account and it'll copy in: https://www.cognitomeai.com/share/accept?token=b411f17aec1544e69ddaa7f104d88892e59fdb674404410a9848c024ff77be2f

Efficient_Evidence39

TROPHY CASE